Brief CV

Xizhao Wang (M’03-SM’08-F’12) was a Research Fellow in the Hong Kong Polytechnic University until 2001 after receiving his doctorate degree in 1998. He served as the Dean of Computer Sciences College in Hebei University, China from 2000 to 2014. In 2014, he joined Shenzhen University, China as a professor and a director of the Big Data Institute.

Dr. Wang’s current research interests include uncertainty-aware machine learning and big data analysis. He pioneered the research on Machine Learning in uncertain environment. His research highlights can be briefly summarized as, digging the distribution of (big) data by modeling uncertainty, and then using distributed parallel technology to design and implement classification and clustering algorithms for different tasks in cybernetics. He published several monographs and textbooks, and over 280 research papers at conferences and in journals in the above topics. His works have been cited 11,675 times since 2016 according to Google scholar. He was selected as a Highly Cited Researcher by Clarivate in 2019 and 2020, in recognition of exceptional research performance demonstrated by production of multiple highly cited papers that rank in the cross-field top 1%.

Dr. Wang was a recipient of the Outstanding Contribution Award in 2004 and the Best Associate Editor Award in 2006 from the IEEE SMCS Society. He served as a member of the Governing Boards in the IEEE SMC Society (2007-2009, 2012-2014). He is an IEEE Fellow (2012), a CAAI Fellow (2017), and the Editor-in-Chief of Springer Journal Machine Learning and Cybernetics (from 2010 to present).

Teaching

Since September 1983, Prof. Wang has delivered for undergraduates, postgraduates, and PhD candidates 50+ courses including the following: Mathematical Analysis, Linear Algebra, Analytic Geometry, Numerical Algebra, Numerical approximation, Discrete Equations, Numerical Solution of Nonlinear Equations, Numerical Solution of Partially Differential Equations, Probability and Statistics, Linear Model, Multi-variant Statistics, Mathematical Modeling, BASIC, FORTRAN, PASCAL, FOXPRO, C/C++, Data Structure, Algorithm Analysis, Artificial Intelligence, Machine Learning, Neural Networks, Genetic Algorithms, Fuzzy Set Theory, Fuzzy Logic, Fuzzy Measures and Integrals, Fuzzy Statistics, Neural Networks and Fuzzy Control, Fuzzy Expert Systems, Neuro-Fuzzy Systems, Data Mining, Statistical Learning Theory, Rough Set Theory and Its Applications

The course "Numerical Approximation" developed and delivered by Prof. Wang and his team was evaluated a Provincial Demonstration Course in Hebei Province in 2004.

From 1996 – present, Prof. Wang has supervised more than 200 PhD or Mphil graduate students. Some of them have grown up as professors in universities, excellent scholars in related academic fields, and managers in technical companies. A part of their names are listed below.

Prof. Wang was recognized as a Model Teacher in China, issued by Education Ministry of China in April 2009.

Honors and Awards

• [14]
  2019-2020 Highly Cited Researcher 2019 and 2020, announced by Clarivate (Web of Science), in recognition of exceptional research performance demonstrated by production of multiple highly cited papers that rank in the top 1% for cross-field.
• [13]
  2014-2020 The Most Cited Chinese Researchers, announced by Elsevier, for exceptional research performance in the field of computer science.
• [12]
  2018 The 8th Wu Wenjun Artificial Intelligence Natural Science Award (2018), Award-winning projects: Uncertain nonlinear system modeling theory and intelligent learning methods, Award level: First Class Prize. Completed by: Song Shiji, Huang Gao, Chen Degang, Wang Xizhao, Wu Cheng. Completed units: Tsinghua University, North China Electric Power University, Shenzhen University.
• [11]
  2016 Leading Scholar, Shenzhen University, China
• [10]
  2015 Overseas High-Caliber Personnel (Level B), Shenzhen Bureau of Human Resources and Social Security, Shenzhen, China
• [9]
  2014 Leading Scholar of Shenzhen City, Shenzhen Bureau of Human Resources and Social Security, Shenzhen, China
• [8]
  2009 Model Teacher in China, issued by Education Ministry of China in April 2009
• [7]
  2009 IEEE Outstanding Committee on Computational Intelligence. Received in the IEEE International Conference on Systems, Man & Cybernetics, October 11-14, 2009, Hyatt Regency River-walk, San Antonio, Texas, USA.
• [6]
  2008 IEEE Outstanding SMCs Chapter Award. Accepted in the IEEE International Conference on Systems, Man & Cybernetics, October 12-15, 2008, Singapore, International Convention and Exhibition Centre.
• [5]
  2007 First-Class Award of Natural Science Advances of Hebei Province in 2007, Feature subset selection of fuzzy-valued attributes (Hebei NSF project).
• [4]
  2007 Second-Class Award of Sciences and Technology Advances in Educational Ministry, China, 2007 (Project Name, Soft computing technology in complex environment and its applications).
• [3]
  2006 IEEE SMC Best Associate Editor Award. Accepted in the IEEE International Conference on Systems, Man & Cybernetics, October 8-11, 2006, Taipei, Taiwan, CHINA.
• [2]
  2004 IEEE SMC Outstanding Contribution Award. Accepted in the IEEE International Conference on Systems, Man Cybernetics, October 12-15, 2004, Netherlands Congress Center, The Hague, HOLLAND.
• [1]
  2002 First-Class Award of Science and Technology Advances in Heilongjiang Province, 2002, Project Name: Research on agricultural expert systems and their development tools.

Membership and Editorship

IEEE Fellow (2012)
CAAI Fellow (2017)
IEEE-SMC Board of Governor Member (2005, 2007-2009, 2012-2014)
Chair, IEEE-SMC Technical Committee on Computational Intelligence (2005-present)
Chair, IEEE SMC Baoding Chapter (2005-2012)
Editor-in-Chief, Springer Journal Machine Learning and Cybernetics (2010-present)
Associate Editor, IEEE Transactions on Cybernetics (2004-present)
Associate Editor, IEEE Transactions on Fuzzy Systems (2018-present)
Associate Editor, Information Sciences (2006-present)
Associate Editor, Pattern Recognition and Artificial Intelligence (2006-2020)
Editorial Member, Chinese Journal of Fuzzy Systems and Mathematics (2005-presnt)
Executive Member, China Association of Artificial Intelligence (CAAI) (the 7th and the 8th)
Chair, CAAI Technical Committee on Knowledge Engineering (the 8th)
Vice-chair, CAAI Technical Committee on Machine Learning (the 7th)

Selected Keynotes and Talks

• [19]
  July 11, 2021, Fuzz-IEEE 2021 (https://attend.ieee.org/fuzzieee-2021/), Tutorial: Uncertainty modeling in adversarial learning (Online)
• [18]
  November 21, 2020, The 4th International Conference on Robotics and Networks, Keynote: Uncertainty modeling in Adversarial Learning (online)
• [17]
  May 25, 2019, Kunming University of science and technology, Talk Title: Limitations of deep learning
• [16]
  August 9, 2018, Jinan, CCDM2018, keynote: Non-iterative deep learning
• [15]
  July 17, 2018, ICMLC2018, Panel Talk – Problems in Deep Learning
• [14]
  July 9, 2018, Rio, Brazil, WCCI / FUZZY-IEEE2018, Tutorial: Learning with Uncertainty
• [13]
  October 29-31, 2017, International Conference of IDEAL, Guilin, Keynote: Big data learning with uncertainty
• [12]
  October 6-7, 2017, The 7th International Conference of ELM, Yantai, Keynote: Big data learning with uncertainty
• [11]
  August 14-15, 2017, The 4th International Conference of SKG, Beijing, Keynote: Learning from big data with uncertainty
• [10]
  December 11-12, 2016, The 3rd International Conference on Fuzzy Systems and Data Mining, Macau, Keynote: Uncertainty in learning from big data
• [9]
  October 12, 2015, SMC2015(IEEE International Conference on Systems, Man, and Cybernetics), Hong Kong, http://www.smc2015.org/, Keynote: Learning from Uncertainty for Big Data
• [8]
  May 12, 2014, Northeast University, Shenyang, China, Talk Title: Learning from Big Data
• [7]
  October 15, 2013, Univ. of Aberystwyth, UK, Talk Title: ELM for Big Data Classification
• [6]
  December 9, 2012, Malaysia University of Science and Technology, Malaysia, Talk Title: Architecture Selection of RWNs and Their Improved Training Algorithms
• [5]
  November 28, 2012, Swinburne University, Melbourne, Australia, Talk Title: Architecture Selection of RWNs and Their Improved Training Algorithms
• [4]
  August 26, 2011, IEEE-SMC Distinguished Lecture Program, Hong Kong Polytechnic University, Hong Kong, Talk Title: Handling Uncertainty in Supervised Learning
• [3]
  June 15, 2010, Henan Normal University, Xinxiang, Talk Title: Inverse Problem of Support Vector Machines and Its Applications
• [2]
  August 12, 2009, the 9th Conference of China Rough Set and Soft Computing, Hebei Normal University, Shijiazhuang, Talk Title: A Comparative Study on Rule Generation Between Decision-tree-based and Rough-set-based Approaches
• [1]
  December 8, 2008, IEEE-SMC Distinguished Lecture Program, National Taiwan University of Science and Technology, Taiwan, Talk Title: Sample Selection Based on Maximum Uncertainty

Research Projects

• [35]
  2024.01-2027.12 (PI), National Natural Science Fund of China, Research on interpretability of deep learning in the view of data bias (Grant #. 62376161, RMB510,000)
• [34]
  2020.01-2023.12 (PI), National Natural Science Fund of China, Uncertainty Study of Meta-learning and its Application in Model Selection (Grant #. 61976141, RMB610,000)
• [33]
  2019.03-2021.01 (PI), Shenzhen Science and Technology Project, Big Data Dependent Classification and Its Application in Stock Market Analysis (Grant #. JCYJ20180305125850156, RMB300,000)
• [32]
  2018.01-2022.12 (Co-PI), Key Project of Natural Science Foundation of China, Uncertainty Modeling Theory and Method for Big Data Machine Learning (Grant #. 61732011, RMB2,850,000)
• [31]
  2017.1-2019.12 (PI), Shenzhen University to introduce high-end talent research funding, Unbalanced Big Data Machine Learning Theory and Algorithm (RMB2,700,000)
• [30]
  2015.8-2017.8 (PI), Shenzhen Science and Technology Project, Complex Relationships and Class Distribution Structure Discovery in Big Data (Grant #. JCYJ20150324140036825，RMB500,000)
• [29]
  2014.01-2017.12 (PI), National Natural Science Fund of China, Data mining in imbalanced large data for electronic health records (Grant #. 71371063, RMB580,000)
• [28]
  2013.01-2015.12 (PI), Natural science fund of Hebei Provience, Study on unceretainty in active learning and its application (Grant #. F2013201110, RMB50,000)
• [27]
  2012.01-2015.12 (PI), National Natural Science Fund of China, Research on uncertainty in supervised learning (Grant #: 61170040, RMB560,000)
• [26]
  2010.01-2012.12 (PI), Hebei Educational Committee, Big data mining based on uncertainty reduction, (Grant #: ZD201013, RMB50,000)
• [25]
  2008.01-2010.12 (PI), Key projects in application basis of Hebei, Research on mining outliers and its applications, (Grant #: 08963522D, RMB100,000)
• [24]
  2008.01-2010.12 (PI), Natural science fund of Hebei, Induction of fuzzy decision tree based on maximum margin and its application to classification of multi-optical spectrum data (Grant #: F2008000635, RMB50,000)
• [23]
  2007.01-2009.12 (PI), A fund to support to the first 100 excellently innovative professional experts (RMB200,000)
• [22]
  2005.09-2008.09 (Co-PI), National Natural Science Fund of China, Statistical learning theory based on uncertainty (Grant #: 60573069, RMB200,000)
• [21]
  2005.01-2007.12 (PI), National Natural Science Fund of China, Research on generalization of weighted fuzzy IT-THEN rules, (Grant #: 60473045, RMB180,000)
• [20]
  2004.08-2006.12 (Co-PI) Hong Kong RGC Fund, Rule extraction based on rough fuzzy sets and reduction of fuzzy attributes
• [19]
  2004.01-2006.12 (PI), Project from China development and reform committee, Intelligent systems of electrical services, (Grant #: 2003-1954, RMB1,200,000)
• [18]
  2003.01-2005.12 (PI), Hebei province Educational Committee, Study on fuzzy neural networks and its application to rule learning (Grant #: B2003117, RMB40,000)
• [17]
  2003.01-2005.12 (PI), Key project from China Educational Ministry, Fuzzy reasoning with interactive factors (Grant #: 03017, RMB50,000)
• [16]
  2002.01-2004.12 (PI), Hebei province Educational Committee, Studies on the Interaction in Fuzzy Reasoning (Grant #. 2002159, RMB15,000)
• [15]
  2003.01-2005.12 (PI), Natural science fund of Hebei, Optimal selection of feature subsets of fuzzy-valued attributes (Grant #: 603137, RMB100,000)
• [14]
  2002.01-2004.12 (Co-PI), Project from Educational Committee in Hebei Province, Access information mining and application on the internet (Grant #. 2001206, RMB10.000)
• [13]
  2002.01-2003.12 (PI), Project from Educational Committee in Hebei Province, Chinese text mining and its applications (Grant #: 2002154, RMB10,000)
• [12]
  2001.01-2002.12 (PI), PhD funded project in Hebei University, Neural Network fusion based on fuzzy integrals (RMB25,000)
• [11]
  1998.09-2001.09 (Research Fellow), Optimal selection of fuzzy feature subsets, Hong Kong RGC project (Grant #: G-T209, HK$200,000; PI: Dr. Eric Tsang)
• [10]
  1998.09-2001.09 (Research Fellow), Fuzzy reasoning based similarity and interaction, Hong Kong RGC project (Grant #: G-T210, HK$200,000; PI: Prof. Daniel Yeung)
• [9]
  1998.09-2001.09 (Research Fellow), System maintenance based on CBR and induction of fuzzy rules, Hong Kong RGC project (Grant #: G-V957, HK$429,880; PI: Dr. Simon Shiu)
• [8]
  1998.09-2001.09 (Research Fellow), Feature weight learning: an approach to maintenance on CBR systems, Hong Kong RGC project (Grant #: A-PA88, HK$180,000; PI: Dr. Eric Tsang)
• [7]
  1998.09-2001.09 (Research Fellow), Validation and verification of fuzzy expert systems based on CBR, department research fund (#: PA25, HK$150,000, PI: Dr. Simon Shiu)
• [6]
  1998.09-2001.09 (Research Fellow), Application of fuzzy technology in hybrid intelligent systems, HK PolyU fund and RGC project (Grant #: G-YY12, HK$1,194,000; PI: Prof. Daniel Yeung)
• [5]
  1998.01-2000.12 (PI), Natural Science Fund of Hebei Province, Theory and algorithms of learning from examples based on fuzzy information (Grant #: 698139, RMB40,000)
• [4]
  1997.06-1998.05 (PI), A project from Institute of LiaoHe oil field exploration, Prediction of oil layers in field of Lei 20-40 (#Lei14-20, RMB120,000)
• [3]
  1997.01-1999.12 (Member), Natural Science Fund of Hebei Province, Fuzzy control theory and its application (Grant #: 97543306D, RMB40,000)
• [2]
  1991.01-1993.06 (Member), Natural Science Fund of Hebei Province, Quantatitive representation of imprecise information and uncertain database theory
• [1]
  1989.01-1992.01 (Member), National Natural Science Fund of China, Mathematical foundation of fuzzy information processing

Published Papers

Recently Accepted Papers

• [Year] - Authors - Paper Title - Journal - [Month]Year - [Volume/Pages] - DOI - Citation numbers (SCI/WOS/GoogleScholar)    |    [Partially updated on 3 March, 2023]
• [315]
  (2024) Zhiming Liu, Jinhai Li, Xiao Zhang, Xizhao Wang. Multi-level information fusion for missing multi-label learning based on stochastic concept clustering. Information Fusion. Accepted in October 2024 (0/0/0)
• [314]
  (2024) Hufsa Khan, Xizhao Wang, Han Liu. Empirical study of outlier impact in classification context; Expert Systems with Applications, Accepted in July 2024 (0/0/0)
• [313]
  (2024) Jie Zhou, Ge Yuan, Can Gao, Xizhao Wang, Jianhua Dai, Witold Pedrycz. Fuzzy Clustering Guided by Spectral Rotation and Scaling; IEEE Transactions on Cybernetics, Accepted in July 2024 (0/0/0)
• [312]
  (2024) Lina Wang, Yichao He, Xizhao Wang, Zihang Zhou, Haibin Ouyang, Seyedali Mirjalili. A novel discrete differential evolution algorithm combining transfer function with modulo operation for solving the multiple knapsack problem, Information Sciences, Accepted in July 2024, DOI: https://doi.org/10.1016/j.ins.2024.121170 (0/0/0)
• [311]
  (2024) Doudou Guo, Weihua Xu, Weiping Ding, Yiyu Yao, Xizhao Wang, Witold Pedrycze and Yuhua Qian; Concept-Cognitive Learning Survey: Mining and Fusing Knowledge from Data; Information Fusion, Accepted in April 2024 (0/0/0)
• [310]
  (2024) Meng Hu, Yanting Guo, Ran Wang, Xizhao Wang; Attribute reduction with fuzzy kernel-induced relations; Information Sciences; Accepted in March 2024 (0/0/0)
• [309]
  (2024) Chong Liu, Yi Wang, Dong Li, Xizhao Wang; Domain-incremental learning without forgetting based on random vector functional link networks; Pattern Recognition; Accepted in March 2024 (0/0/0)
• [308]
  (2024) Tianlun Zhang, Xinlei Zhou, Debby D. Wang, Xizhao Wang; Feature Similarity Learning Based on Fuzziness Minimization for Semi-supervised Medical Image Segmentation; Information Fusion; Accepted in January 2024 (0/0/0)
• [307]
  (2023) Yichao He, Jinghong Wang, Xuejing Liu, Xizhao Wang, Haibin Ouyang; Modelling and solving of knapsack problem with setup based on evolutionary algorithm; Mathematics and Computers in Simulation;December 2023; Volume: 219, Pages: 378-403; DOI: 10.1016/j.matcom.2023.12.033 (0/0/0)
• [306]
  (2023) Hongjie Xing, Weitao Liu, Xizhao Wang; Bounded exponential loss function based AdaBoost ensemble of OCSVMs; Pattern Recognition; Accepted in December 2023; DOI: https://doi.org/10.1016/j.patcog.2023.110191 (0/0/0)
• [305]
  (2023) Shuyue Chen, Jiaolv Wu, Jian Lu, Xizhao Wang; A mathematical model for efficient extraction of key locations from point-cloud data in track area; Industrial Artificial Intelligence; Accepted in July 2023; DOI: https://doi.org/10.1007/s44244-023-00011-5 (0/0/0)
• [304]
  (2023) Zhiming Liu, Jinhai Li, Xiao Zhang, Xizhao Wang; Incremental Incomplete Concept-Cognitive Learning Model: A Stochastic Strategy; IEEE Transactions on Neural Networks and Learning Systems; Accepted in November 2023; DOI: 10.1109/TNNLS.2023.3333537 (0/0/0)
• [303]
  (2023) Yan Li, Xiaoxue Wu, Xizhao Wang; Incremental reduction methods based on granular ball neighborhood rough sets and attribute grouping; International Journal of Approximate Reasoning, Accepted in July 2023; DOI: https://doi.org/10.1016/j.ijar.2023.108974 (0/0/0)
• [302]
  (2023) Si Cen, Xizhao Wang, Xiaoying, Chao Liu, Guoquan Dai; New Attention Strategy for Negative Sampling in Knowledge Graph Embedding; Applied Intelligence, Accepted in July 2023; DOI: 10.1007/s10489-023-04901-0 (0/0/0)
• [301]
  (2023) Tianlun Zhang and Xizhao Wang; Anchor-Wise Fuzziness Modeling in Convolution-Transformer Neural Networks for Left Atrium Image Segmentation; IEEE Transactions on Fuzzy Systems; Accepted in July 2023; Volume: 32, Issue: 2; Page(s): 398-408; DOI:10.1109/TFUZZ.2023.3298904 (0/0/0)
• [300]
  (2023) Qin Wang, Jingna Liu, Wenwu Guo and Xizhao Wang; Evolving stochastic configure network: A more compact model with interpretability; Information Sciences; Accepted in April 2023 (0/0/0)
• [299]
  (2023) Kaijian Chen, Jingna Liu, Wenwu Guo, Xizhao Wang; A two-stage approach based on Bayesian deep learning for predicting remaining useful life of rolling element bearings; Computers and Electrical Engineering; Accepted in April 2023 (0/0/0)
• [298]
  (2023) Jie Zhou, Can Gao, Xizhao Wang, Zhihui Lai, Jun Wan, Xiaodong Yue, Typicality-Aware Adaptive Similarity Matrix for Unsupervised Learning; IEEE Transactions on Neural Networks and Learning Systems; Accepted in February 2023 (0/0/0)
• [297]
  (2023) Arun Kumar, Xizhao Wang, et al; Guest Editorial Cognitive Cyber-Physical Systems with AI Based Solutions in Medical Informatics; IEEE Journal of Biomedical and Health Informatics, FEBRUARY 2023, Volume: 27, Issue: 2, Pages: 586-587, DOI: 10.1109/JBHI.2023.3234603 (0/0/0)
• [296]
  (2023) Hufsa Khan, Han Liu, Xizhao Wang; A Study on relationship between prediction uncertainty and robustness to noisy data; International Journal of Systems Science (TSYS), Accepted in January 2023 (0/0/0)
• [295]
  (2022) Shiping Wang, Xincan Lin, Yiqing Shi, Xizhao Wang; Algorithm for orthogonal matrix nearness and its application to feature representation; Information Sciences; Accepted in December 2022 (0/0/0)
• [294]
  (2022) Min Wang; Peng Zhao; Xin Lu; Fan Min; Xizhao Wang; Fine-Grained Visual Categorization: A Spatial–Frequency Feature Fusion Perspective; IEEE Transactions on Circuits and Systems for Video Technology; JUNE 2023; Volume: 33, Issue: 6; Page(s): 2798-2812; Digital Object Identifier: 10.1109/TCSVT.2022.3227737 (0/0/0)
• [293]
  (2022) Xinlei Zhou, Sudong Chen, Nianjiao Peng, Xinpeng Zhou and Xizhao Wang; Uncertainty Guided Pruning of Classification Model Tree; Knowledge-Based Systems; Accepted in October 2022 (0/0/0)
• [292]
  (2022) Weipeng Cao, Yuhao Wu, Chengchao Huang, Muhammed J. A. Patwary and Xizhao Wang; MFF: Multi-Modal Feature Fusion for Zero-Shot Learning; Neurocomputing; Accepted in September 2022 (0/0/0)
• [291]
  (2022) Chao Liu, Xizhao Wang, Han Liu, Xiaoying Zou, Si Cen, and Guoquan Dai; Learning to Recommend Journals for Submission Based on Embedding Models; Neurocomputing; Accepted in August 2022 (0/0/0)
• [290]
  (2022) Shuyue Chen, Ran Wang, Jian Lu, and Xizhao Wang; Stable Matching-Based Two-Way Selection in Multi-Label Active Learning with Imbalanced Data; Information Sciences; Accepted in July 2022 (0/0/0)
• [289]
  (2022) Yanting Guo, Meng Hu, Xizhao Wang, Eric C.C. Tsang, Degang Chen, and Weihua Xu; A robust approach to attribute reduction based on double fuzzy consistency measure; Knowledge-Based Systems; Accepted in July 2022 (0/0/0)
• [288]
  (2022) Ying Zhao, Shuang Li, Rui Zhang, Chi Harold Liu, Weipeng Cao, Xizhao Wang and Song Tian; Semantic Correlation Transfer for Heterogeneous Domain Adaptation; IEEE Transactions on Neural Networks and Learning Systems, Accepted in August 2022; DOI: 10.1109/TNNLS.2022.3199619 (0/0/0)
• [287]
  (2023) Jianhua Dai, Xiongtao Zou, Yuhua Qian, Xizhao Wang; Multi-Fuzzy Beta-Covering Approximation Spaces and Their Information Measures; IEEE Transactions on Fuzzy Systems, March 2023, 31(3): 955-969; DOI: 10.1109/TFUZZ.2022.3193448 (0/0/0)
• [286]
  (2022) Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, Xizhao Wang; A Review of Generalized Zero-Shot Learning Methods; IEEE Transactions on Pattern Analysis and Machine Intelligence; Accepted in July 2022 (0/0/36)
• [285]
  (2022) Guoquan Dai, Xizhao Wang, Xiaoying Zou, Chao Liu, Si Cen; MRGAT: Multi-Relational Graph Attention Network for Knowledge Graph Completion; Neural Networks; Accepted in July 2022 (0/0/0)
• [284]
  (2022) Xiaoying Zou, Xizhao Wang, Si Cen, Guoquan Dai, Chao Liu; Knowledge graph embedding with self-adaptive double-limited loss; Knowledge-Based Systems; Accepted in June 2022. DOI: https://doi.org/10.1016/j.knosys.2022.109310 (0/0/0)
• [283]
  (2023) Wentao Li, Haoxiang Zhou, Weihua Xu, Xizhao Wang, Witold Pedrycz; Interval Dominance-Based Feature Selection for Interval-Valued Ordered Data; IEEE Transactions on Neural Networks and Learning Systems; October 2023; Volume: 34, Number: 10, Page(s):6898-6912, DOI: 10.1109/TNNLS.2022.3184120. (0/0/0)
• [282]
  (2023) Min Wang, Chunyu Yang, Fei Zhao, Fan Min, Xizhao Wang; Cost-Sensitive Active Learning for Incomplete Data: IEEE Transactions on Systems, Man, and Cybernetics: Systems; January 2023; Volume: 53, Issue: 1; Pages 405-416; DOI: 10.1109/TSMC.2022.3182122
• [281]
  (2022) Juncheng Li, Faming Fang, Tieyong Zeng, Guixu Zhang, Xizhao Wang; Adjustable Super-Resolution Network via Deep Supervised Learning and Progressive Self-Distillation; Neurocomputing; Accepted in May 2022. (0/0/0)
• [280]
  (2022) Farhad Pourpanah, Ran Wang, Chee Peng Lim, Xi-Zhao Wang, Danial Yazdani, A Review of Artificial Fish Swarm Algorithms: Recent Advances and Applications, Artificial Intelligence Review. Accepted in May 2022. (0/0/0)
• [279]
  (2022) Muhammed J. A. Patwary, Weipeng Cao, Xizhao Wang(*), Mohammad Ahsanul Haque. Fuzziness based semi-supervised multimodal learning for patient’s activity recognition using RGBDT videos. Applied Soft Computing. Accepted (available online) 25 February 2022. (0/0/0)
• [278]
  (2022) Hufsa Khan, Xizhao Wang, Han Liu(*). Handling missing data through deep convolutional neural network. Information Sciences. Accepted in February 2022. Available online 1 March 2022. https://doi.org/10.1016/j.ins.2022.02.051. (0/0/0)
• [277]
  (2022) Sihong Chen, Haojing Shen, Ran Wang, Xizhao Wang(*). Towards improving fast adversarial training in multi-exit network. Neural Networks. Accepted in February 2022. https://doi.org/10.1016/j.neunet.2022.02.015 (0/0/0)
• [276]
  (2023) Haojing Shen, Sihong Chen, Ran Wang, Xizhao Wang(*). Adversarial Learning with Cost-Sensitive Classes. IEEE Transactions on Cybernetics. AUGUST 2023, Volume: 53, Issue: 8, Page(s): 4855-4866 ; DOI: https://doi.org/10.1109/TCYB.2022.3146388 (0/0/0)
• [275]
  (2022) 王静红，梁丽娜，李昊康，王熙照 ; 基于标记注意力机制的社区发现算法; 山东大学学报(理学版),2022 (0/0/0)
• [274]
  (2022) 周欣蕾，王熙照; 不确定性视角下的弱监督学习 西北大学学报(自然科学版), 5(52)2022, 813-823页 (0/0/0)
• [273]
  (2022) 陈思宏; 沈浩靖; 王冉; 王熙照(*); 预测不确定性与对抗鲁棒性的关系研究.软件学报,2022年1月,33(2):524-538，DOI:10.13328/j.cnki.jos.006163(期刊论文) (0/0/0)

IEEE Papers

• [272]
  (2021) Mei Yang, Yu-Xuan Zhang, Xizhao Wang and Fan Min(*). Multi-Instance Ensemble Learning with Discriminative Bags. IEEE Transactions on Systems, Man and Cybernetics: Systems. Accepted in November 2021, doi: https://doi.org/10.1109/TSMC.2021.3125040 (0/0/0)
• [271]
  (2022) Hong Zhu, Xizhao Wang(*) and Ran Wang(*). Fuzzy Monotonic K-Nearest Neighbor versus Monotonic Fuzzy K-Nearest Neighbor. IEEE Transactions on Fuzzy Systems, September 2022, Volume 30, Issue 9, Pages 3501-3513. DOI: https://doi.org/10.1109/TFUZZ.2021.3117450 (0/0/0)
• [270]
  (2021) Suyun Zhao(*), Zhigang Dai, Xizhao Wang, Peng Ni, Hengheng Luo, Hong Chen, Cuiping Li, An Accelerator for Rule Induction in Fuzzy Rough Theory, IEEE Transactions on Fuzzy Systems, Vol.29 (12), 3635-3649, December 2021, DOI: https://doi.org/10.1109/TFUZZ.2021.3101935 (0/0/0)
• [269]
  (2021) Jianhui Pang, Yanghui Rao(*), Haoran Xie, Xizhao Wang, Fu Lee Wang, Tak-Lam Wong, Qing Li, Fast Supervised Topic Models for Short Text Emotion Detection. IEEE Transactions on Cybernetics, February 2021, 51(2): 815-828, doi: https://doi.org/10.1109/TCYB.2019.2940520 (4/4/13)
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  (2020) Dongmei Mo, Zhihui Lai, Waikeung Wong(*), Xizhao Wang. Jointly Sparse Locality Regression for Image Feature Extraction. IEEE Transactions on Multimedia, November 2020, 22(11): 2873-2888, doi: https:// doi.org/10.1109/TMM.2019.2961508 (0/0/1)
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  (2020) Lei Zhang(*), Qingyan Duan, David Zhang, Wei Jia, Xizhao Wang. AdvKin: Adversarial Convolutional Network for Kinship Verification. IEEE Transactions on Cybernetics, December 2021, 51(12):5883-5896, 1-14 doi: https:// doi.org/10.1109/TCYB.2019.2959403 (7/7/16)
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  (2019) Qin Lin, Huailing Zhang, Xizhao Wang(*), Yun Xue, Hongxin Liu, Changwei Gong. A Novel Parallel Biclustering Approach and Its Application to Identify and Segment Highly Profitable Telecom Customers. IEEE Access, December 2019, 7(1): 28696-28711, doi: https://doi.org/10.1109/ACCESS.2019.2898644 (2/2/7)
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  (2019) Rong Chen, Shikai Guo, Xizhao Wang(*), Tianlun Zhang. Fusion of Multi-RSMOTE with Fuzzy Integral to Classify Bug Reports with an Imbalanced Distribution. IEEE Transactions on Fuzzy Systems, December 2019, 27(12):2406-2420, doi: https://doi.org/10.1109/TFUZZ.2019.2899809 (49/49/55)
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  (2019) Salim Rezvani, Xizhao Wang(*), Farhad Pourpanah. Intuitionistic Fuzzy Twin Support Vector Machines. IEEE Transactions on Fuzzy Systems, November 2019, 27(11):2140-2151, doi: https://doi.org/10.1109/TFUZZ.2019.2893863 (26/28/41)
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  (2019)Laizhong Cui, Chong Xu, Shu Yang(*), Joshua Zhexue Huang, Jianqiang Li, Xizhao Wang, Zhong Ming, Nan Lu. Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing for Internet of Things. IEEE Internet of Things Journal, June 2019, 6(3): 4791-4803, doi: https://doi.org/10.1109/JIOT.2018.2869226 (40/42/59)
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  (2019) Wing W.Y. Ng, Xing Tian(*), Witold Pedrycz, Xizhao Wang, Daniel S. Yeung. Incremental Hash-bit Learning for Semantic Image Retrieval in Non-stationary Environments. IEEE Transactions on Cybernetics, November 2019, 49: 3844-3858, doi: https://doi.org/10.1109/TCYB.2018.2846760 (0/0/10)
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  (2019) Xiaojun Chen(*), Wenya Sun, Bo Wang, Zhihui Li, Xizhao Wang, Yunming Ye. Spectral Clustering of Customer Transaction Data With a Two-Level Subspace Weighting Method. IEEE Transactions on Cybernetics, September 2019, 49(9):3230-3241, doi: https://doi.org/10.1109/TCYB.2018.2836804 (9/9/19)
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  (2019) Wing W. Y. Ng, Jianjun Zhang, Chun Sing Lai(*), Witold Pedrycz, Loi Lei Lai(*), and Xizhao Wang. Cost-Sensitive Weighting and Imbalance-Reversed Bagging for Streaming Imbalanced and Concept Drifting in Electricity Pricing Classification. IEEE Transactions on Industrial Informatics, March 2019, 15(3):1588-1597, doi: https://doi.org/10.1109/TII.2018.2850930 (15/15/21)
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  (2019) Xizhao Wang, Tianlun Zhang, Ran Wang(*). Noniterative Deep Learning: Incorporating Restricted Boltzmann Machine into Multilayer Random Weight Neural Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, July 2019, 49(7):1299-1380, doi: https://doi.org/10.1109/TSMC.2017.2701419 (35/35/68)
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  (2018) Yanyan Yang(*), Degang Chen, Hui Wang, Xizhao Wang. Incremental perspective for feature selection based on fuzzy rough sets. IEEE Transactions on Fuzzy Systems, June 2018, 26(3):1257-1273, doi: https://doi.org/10.1109/TFUZZ.2017.2718492 (40/42/52)
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  (2018) Patrick P. K. Chan, Weiwen Liu, Danni Chen, Daniel S. Yeung, Fei Zhang(*), Xizhao Wang, Chien-Chang Hsu. Face Liveness Detection Using a Flash Against 2D Spoofing Attack. IEEE Transactions on Information Forensis and Security, February 2018, 13(2):521-534, doi: https://doi.org/10.1109/TIFS.2017.2758748 (26/27/52)
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  (2018) Xizhao Wang, Ran Wang(*), Chen Xu. Discovering the Relationship Between Generalization and Uncertainty by Incorporating Complexity of Classification. IEEE Transactions on Cybernetics, February 2018, 48(2):703-715,doi: https://doi.org/10.1109/TCYB.2017.2653223 (71/71/85)
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  (2017) Changzhong Wang(*), Qinghua Hu, Xizhao Wang, Degang Chen, Yuhua Qian, Zhe Dong. Feature selection based on neighborhood discrimination index. IEEE Transactions on Neural Networks and Learning Systems, July 2017, 29(7):2986-2999, doi: https://doi.org/10.1109/TNNLS.2017.2710422 (112/124/129)
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  (2017) Ran Wang, Xizhao Wang (*), Sam Kwong, Chen Xu. Incorporating Diversity and Informativeness in Multiple-Instance Active Learning. IEEE Transactions on Fuzzy Systems, December 2017, 25(6): 1460-1475, doi: https://doi.org/10.1109/TFUZZ.2017.2717803 (65/66/78)
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  (2016) Xizhao Wang(*), Yulin He. Learning from Uncertainty for Big Data (Future Analytical Challenges and Strategies). IEEE Systems, Man, and Cybernetics Magazine, April 2016, 2(2): 26-31, doi: https://doi.org/10.1109/MSMC.2016.2557479 (2/2/39)WOS NO FOUND
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  (2014) Huimin Feng and Xizhao Wang(*). Performance Improvement of Classifier Fusion for Batch Samples Based on Upper Integral. Neural Networks, March 2015, 63: 87-93, doi: https://doi.org/10.1016/j.neunet.2014.11.004 (6/6/7)
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  (2012) Xizhao Wang(*), Lingcai Dong, Jianhui Yan. Maximum ambiguity based sample selection in fuzzy decision tree induction. IEEE Transactions on Knowledge and Data Engineering, August 2012, 24(8): 1491-1505, doi: https://doi.org/10.1109/TKDE.2011.67 (127/133/168)
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  (2010) Suyun Zhao(*), Eric C. C. Tsang, Degang Chen, Xizhao Wang. Building a rule-based classifier-a fuzzy-rough set approach, IEEE Transactions on Knowledge and Data Engineering, May 2010, 22(5): 624-638, doi: https://doi.org/10.1109/TKDE.2009.118 (87/93/115)
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  (2009) Xizhao Wang(*), Chunru Dong. Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Transactions on Fuzzy Systems, June2009, 17(3): 556-567, doi: https://doi.org/10.1109/TFUZZ.2008.924342 (160/161/207)
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Elsevier Papers

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  (2021) Xinlei Zhou, Han Liu, Farhad Pourpanah, Tieyong Zeng and Xizhao Wang(*). A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications. Neurocomputing. Accepted in November 2021 (0/0/0)
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  (2020) Tianlun Zhang, Xi Yang, Xizhao Wang, Ran Wang(*). Deep Joint Neural Model for Single Image Haze Removal and Color Correction. Information Sciences, December 2020, 541:16-35, doi: https://doi.org/10.1016/j.ins.2020.05.105 (0/0/2)
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  (2020) Kai Zhang, Jianming Zhan(*), Xizhao Wang. TOPSIS-WAA method based on a covering-based fuzzy rough set: an application to rating problem. Information Science, October 2020, 539: 397-421, doi: https://doi.org/10.1016/j.ins.2020.06.009 (25/26/30)
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  (2020) Jafar Gholami, Farhad Pourpanah, Xizhao Wang(*), Feature selection based on improved binary global harmony search for data classification. Applied Soft Computing, August 2020, 93: 106402, doi: https://doi.org/10.1016/j.asoc.2020.106402 (14/14/19)
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  Peng Ni, Suyun Zhao(*), Xizhao Wang, Hong Chen, Cuiping Li. PARA: A Positive-region based Attribute Reduction Accelerator. Information Science, November 2019, 503: 533-550, doi: https://doi.org/10.1016/j.ins.2019.07.038 (12/13/16)
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  (2020) Xinlei Zhou, Xizhao Wang(*), Cong Hu, Ran Wang. An analysis on the relationship between uncertainty and misclassification rate of classifiers. Information Sciences, October 2020, 535: 16-27, doi: https://doi.org/10.1016/j.ins.2020.05.059 (1/1/3)
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  (2020) Peng Ni, Suyun Zhao(*), Xizhao Wang, Hong Chen, Cuiping Li, Eric C.C. Tsang. Incremental Feature Selection Based on Fuzzy Rough Sets. Information Sciences, October 2020, 536: 185-204, doi: https://doi.org/10.1016/j.ins.2020.04.038 (10/10/15)
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  (2020) Yuxuan Luo, Xizhao Wang(*), Weipeng Cao. A Novel Dataset-Specific Feature Extractor for Zero-Shot Learning. Neurocomputing, May 2020, 391:74-82, doi: https://doi.org/10.1016/j.neucom.2020.01.069 (4/4/6)
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  (2020) Xingping Xian, Tao Wu(*), Shaojie Qiao(*), Xi-Zhao Wang, Wei Wang, Yanbing Liu. NetSRE: Link predictability measuring and regulating. Knowledge-Based Systems, May 2020, 196:105800, doi: https://doi.org/10.1016/j.knosys.2020.105800 (4/4/5)
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  (2020) MingWen Shao(*), WeiZhi Wu, XiZhao Wang, ChangZhong Wang. Knowledge reduction methods of covering approximate spaces based on concept lattice. Knowledge-Based Systems, March 2020, 191: 105269, doi: https://doi.org/10.1016/j.knosys.2019.105269 (4/4/5)
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  (2020) S. Rezvani and Xizhao Wang(*). Erratum to “Entropy-based fuzzy support vector machine for imbalanced datasets" [Knowl.-Based Syst. 115 (2017) 87–99]. Knowledge-Based Systems, March 2020, 192: 105287, doi: https://doi.org/10.1016/j.knosys.2016.09.032 (0/0/3)WOS NO FOUND
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  (2020) Tianlun Zhang, Yang Li, Xizhao Wang(*). Gaussian prior based adaptive synthetic sampling with non-linear sample space for imbalanced learning. Knowledge-Based Systems, March 2020, 191: 105231, doi: https://doi.org/10.1016/j.knosys.2019.105231 (2/2/3)
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  (2019) Farhad Pourpanah, Ran Wang(*), Chee Peng Lim, Xizhao Wang, Manjeevan Seera, Choo Jun Tan. An Improved Fuzzy ARTMAP and Q-Learning Agent Model for Pattern Classification. Neurocomputing, September 2019, 359: 139-152, doi: https://doi.org/10.1016/j.neucom.2019.06.002 (10/10/17)
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  (2019) Dasen Yan, Xinlei Zhou, Xizhao Wang, Ran Wang(*). An Off-centered Technique: Learning a Feature Transformation to Improve the Performance of Clustering and Classification. Information Science, November 2019, 503: 635-651, doi: https://doi.org/10.1016/j.ins.2019.06.068 (2/2/3)
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  (2019) Li Zhao, Xizhao Wang(*). Seemingly Unrelated Extreme Learning Machine. Neurocomputing, August 2019, 355:134–142, doi: https://doi.org/10.1016/j.neucom.2019.04.067 (4/4/4)
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  (2019) Muhammed J.A. Patwary, XiZhao Wang(*). Sensitivity analysis on initial classifier accuracy in fuzziness based semi-supervised learning, Information Sciences, July 2019, 490: 93-112, doi: https://doi.org/10.1016/j.ins.2019.03.036 (8/8/16)
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  (2019) Farhad Pourpanah(*), Chee Peng Lim, Xizhao Wang, Choo Jun Tan, Manjeevan Seera, Yuhui Shi. A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification. Neurocomputing, March 2019, 333: 440-45, doi: https://doi.org/10.1016/j.neucom.2019.01.011 (24/24/36)
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  (2019) Yichao He, Xizhao Wang(*), Suogang Gao. Ring Theory-Based Evolutionary Algorithm and its application to D{0–1}KP. Applied Soft Computing, April 2019, 77: 714–22, doi: https://doi.org/10.1016/j.asoc.2019.01.049 (5/5/9)
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  (2018) Huang Z, Wang X(*). Sensitivity of data matrix rank in non-iterative training. Neurocomputing, November 2018, 313(3):386–391, DOI: https://doi.org/10.1016/j.neucom.2018.06.055 (4/4/4)
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  (2021) Yichao He, Xizhao Wang(*). Group theory-based optimization algorithm for solving knapsack problems. Knowledge-Based Systems, May 2021, 104445, doi: https://doi.org/10.1016/j.knosys.2018.07.045 (11/12/18)
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  (2018) Laizhong Cui, Kai Zhang, Genghui Li(*), XiZhao Wang, Shu Yang. A smart artificial bee colony algorithm with distance-fitness-based neighbor search and its application. Future Generation Computer Systems, December 2018, 89:478-493, doi: https://doi.org/10.1016/j.future.2018.06.054 (15/15/18)
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  (2018) Junhai Zhai(*), Xizhao Wang, Sufang Zhang, Shaoxing Houd. Tolerance rough fuzzy decision tree. Information Sciences, October 2018, 465:425-438, doi: https://doi.org/10.1016/j.ins.2018.07.006 (12/12/18)
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  (2018) Zhi Wang and Xizhao Wang(*). A deep stochastic weight assignment network and its application to chess playing. Journal of Parallel and Distributed Computing, July 2018, 117: 205-211, doi: https://doi.org/10.1016/j.jpdc.2017.08.013 (8/8/9)
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  (2018) Wing W.Y. Ng, Xiancheng Zhou, Xing Tian(*), Xizhao Wang, Daniel S. Yeung. Bagging–boosting-based semi-supervised multi-hashing with query-adaptive re-ranking. Neurocomputing, January 2018, 275:916-923, doi: https://doi.org/10.1016/j.neucom.2017.09.042 (17/18/22)
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  (2018) Yichao He, Haoran Xie, Tak-Lam Wong, Xizhao Wang(*), A novel binary artificial bee colony algorithm for the set-union knapsack problem, Future Generation Computer Systems, January 2018, 78: 77-86, doi: https://doi.org/10.1016/j.future.2017.05.044 (50/56/72)
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  (2018) Weipeng Cao, Xizhao Wang(*), Zhong Ming, Jinzhu Gao. A Review on Neural Networks with Random Weights. Neurocomputing, January 2018, 275:278–287, doi: https://doi.org/10.1016/j.neucom.2017.08.040 (180/182/272)
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  (2017) Hongyu Zhu, Xi-Zhao Wang(*). A cost-sensitive semi-supervised learning model based on uncertainty. Neurocomputing, August 2017, 251: 106–114, doi: https://doi.org/10.1016/j.neucom.2017.04.010 (14/17/20)
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  (2017) Hong Zhu, Eric Tsang(*), Xizhao Wang, Rana Aamir Raza Ashfaq. Monotonic classification Extreme Learning Machine. Neurocomputing, February 2017, 225: 205–213, doi: https://doi.org/10.1016/j.neucom.2016.11.021 (26/28/35)
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  (2017) Rana Aamir Raza Ashfaq, Xi-Zhao Wang(*), Joshua Zhexue Huang, Haider Abbas, Yu-Lin He. Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, February 2017, 378: 484–497, doi: https://doi.org/10.1016/j.ins.2016.04.019 (259/273/466)
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  (2017) Hong-Jie Xing(*), Xi-Zhao Wang. Selective ensemble of SVDDs with Renyi entropy based diversity measure. Pattern Recognition, January 2017, 61: 185-196. doi: https://doi.org/10.1016/j.patcog.2016.07.038 (11/12/11)
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  (2016) Mingwen Shao(*), Yee Leung, Xizhao Wang, WeiZhi Wu, Granular reducts of formal fuzzy contexts, Knowledge-Based Systems, October 2016, 114:156-166, doi: https://doi.org/10.1016/j.knosys.2016.10.010 (22/30/32)
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  (2016) Junhai Zhai(*), Xizhao Wang, Xiaohe Pang. Voting-based Instance Selection from Large Data Sets with MapReduce and Random Weight Networks. Information Sciences, November 2016, 367: 1066–1077, doi: https://doi.org/10.1016/j.ins.2016.07.026 (34/38/43)
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  (2016) Yi-Chao He, Xi-zhao Wang(*), Yu-Lin He, Shu-Liang Zhao, Wen-Bin Li. Exact and approximate algorithms for discounted {0-1} knapsack problem. Information Sciences, November 2016, 369: 634–647, doi: https://doi.org/10.1016/j.ins.2016.07.03 (22/34/37)
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  (2016) Yu-Lin He, Xi-zhao Wang(*), Joshua Zhexue Huang, Fuzzy nonlinear regression analysis using a random weight network, Information Sciences, October 2016, 364: 222-240, doi: https://doi.org/10.1016/j.ins.2016.01.037 (98/98/113)
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  (2016) Huanyu Zhao, Zhaowei Dong, Tongliang Li(*), Xizhao Wang, Chaoyi Pang. Segmenting time series with connected lines under maximum error bound. Information Sciences, June 2016, 345:1-8, doi: https://doi.org/10.1016/j.ins.2015.09.017 (14/15/19)
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  (2015) Yu-lin He(*), James N.K. Liu, Yan-xing Hu, Xi-zhao Wang. OWA operator based link prediction ensemble for social network. Expert Systems with Applications, January 2015, 42: 21-50, doi: https://doi.org/10.1016/j.eswa.2014.07.018 (92/104/113)
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  (2015) Xi-zhao Wang(*), Zhe-Xue Huang. Editorial: Uncertainty in learning from big data. Fuzzy Sets and Systems, January 2015, 258: 1-4, doi: https://doi.org/10.1016/j.fss.2014.10.010 (16/16/28)
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  (2014) Yulin He(*), Ran Wang, Sam Kwong, Xizhao Wang. Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis. Information Sciences, February 2014, 259(3):252-268, doi: https://doi.org/10.1016/j.ins.2013.09.003 (46/55/62)
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  (2014) Chunru Dong, Wing W.Y. Ng(*), Xizhao Wang, Patrick P.K.Chan, Daniel S.Yeung. An improved differential evolution and its application to determining feature weights in similarity-based Clustering. Neurocomputing, December 2014, 146: 95-103, doi: https://doi.org/10.1016/j.neucom.2014.04.065 (19/19/28)
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  (2014) Aimin Fu, Xizhao Wang, Yulin He(*), Laisheng Wang. A study on residence error of training an extreme learning machine and its application to evolutionary algorithms. Neurocomputing, December 2014, 146(1): 75-82, doi: https://doi.org/10.1016/j.neucom.2014.04.067 (15/16/17)
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  (2014) Hongyan Ji, Xizhao Wang(*), Yulin He, Wenliang Li. A study on relationships between heuristics and optimal cuts in decision tree induction. Computers and Electrical Engineering, July 2014, 40: 1429-1438, doi: https://doi.org/10.1016/j.compeleceng.2013.11.030 (1/1/2)
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  (2013) Lisha Hu(*), Shuxia Lu(*), Xizhao Wang. A New and Informative Active Learning Approach for Support Vector Machine. Information Sciences, September 2013, 244: 142-160, doi: https://doi.org/10.1016/j.ins.2013.05.010 (21/24/28)
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  (2013) Suyun Zhao(*), Xizhao Wang. Degang Chen and Eric Tsang. Nested structure in parameterized rough reduction, November 2013, 248: 130-150, doi: https://doi.org/10.1016/j.ins.2013.05.039 (25/28/28)
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  (2013) Xizhao Wang(*), Qingyan Shao, Miao Qing, Junhai Zhai. Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing, February 2013, 102: 3-9, doi: https://doi.org/10.1016/j.neucom.2011.11053 (74/76/102)
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  (2012) Qiang Hua(*), Lijie Bai, Xizhao Wang. Local similarity and diversity preserving discriminant projection for face and handwriting digits recognition. NeuroComputing, June 2012, 86:150-157, doi: https://doi.org/10.1016/j.neucom.2012.01.031 (18/19/19)
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Conference Papers

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  (2014) Junhai Zhai(*), Jinggeng Wang, Xizhao Wang, Ensemble online sequential extreme learning machine for large data set classification, Proceedings of 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, USA, 05-08 October 2014 , Proceedings pp. 2250-2255, DOI: 10.1109/SMC.2014.6974260 (11/8/11)
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  (2012) Xizhao Wang(*), Meng Zhang, Shuxia Lu, Xu Zhou, A total error rate multi-class classification, In Proceedings of 2012 International Conference on Systems, Man, and Cybernetics, Seoul, South Korea, October 2012, Proceedings pp: 964-969, doi: https://doi.org/10.1109/ICSMC.2012.6377853 (0/0/0)
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  (2012) Xizhao Wang(*), Qing Miao, Mengyao Zhai, Junhai Zhai, Instance selection based on sample entropy for efficient data classification with ELM, In Proceedings of 2012 International Conference on Systems, Man, and Cybernetics, Seoul, South Korea, October 2012, Proceedings pp: 970-974, doi: https://doi.org/10.1109/ICSMC.2012.6377854 (2/2/2)
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  (2011) Hongjie Xing(*), Xizhao Wang, Minghu Ha, A comparative experimental study of feature-weight learning approaches, In Proceedings of 2011 International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA, October 2011 , Proceedings pp: 3500-3505, DOI: 10.1109/ICSMC.2011.6084211 (4/3/5)
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  (2011) Junhai Zhai(*), Yuanyuan Gao, Mengyao Zhai, Xizhao Wang, Rough set model and its eight extensions, In Proceedings of 2011 International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA, October 2011, Proceedings pp: 3512-3517, doi: https://doi.org/10.1109/ICSMC.2011.6084213 (0/2/3)
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  (2011) James N. K. Liu(*), Yulin He, Xizhao Wang, Yanxing Hu, A comparative study among different kernel functions in flexible naïve Bayesian classification, In Proceedings of 2011 International Conference on Machine Learning and Cybernetics, Guilin, China, July 2011 Proceedings pp: 638-643, doi: https://doi.org/10.1109/ICMLC.2011.6016813 (7/0/7)
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  (2010) Xizhao Wang(*), Xianghui Gao, Qiang He, Side effect of cut in decision tree generation for continuous attributes, In Proceedings of 2010 International Conference on Systems, Man, and Cybernetics, Istanbul, Turkey, October 2010, Proceedings pp: 1364-1369, doi: https://doi.org/10.1109/ICSMC.2010.5642456 (1/0/1)
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  (2009) Shan Su(*), Xizhao Wang, Junhai Zhai, An Improved Cluster Oriented Fuzzy Decision Trees, In Proceedings of 2009 International Conference on Rough Sets, Fuzzy Sets , Data Mining & Granular Computing, Indian Inst Technol, Delhi, India, December 2005-2009, Proceedings pp: 447-454, doi: https://doi.org/10.1007/978-3-642-10646-0_54 (1/1/1)
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  (2009) Ling-Cai Dong(*), Dan Wang, Xi-Zhao Wang, An Improved Sample Selection Algorithm in Fuzzy Decision Tree Induction, In Proceedings of 2009 International Conference on Systems, Man, and Cybernetics, San Antonio, TX, October 2009 , Proceedings pp: 629-634, doi: https://doi.org/10.1109/ICSMC.2009.5346654 (5/5/5)
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  (2009) Mingzhu Lu(*), Philip Chen, Jianbing Huo, Xizhao Wang, Multi-Stage Decision Tree based on Inter-class and Inner-class Margin of SVM, In Proceedings of 2009 International Conference on Systems, Man, and Cybernetics, San Antonio, TX, USA, October 2009, Proceedings pp: 1875-1880, doi: https://doi.org/10.1109/ICSMC.2009.5346208 (12/8/18)
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  (2008) Ning Zhang(*), Xizhao Wang, Tao Xiao, An Instance Selection Algorithm Based on Contribution, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, China, July 2008, Proceedings pp: 919-923, doi: https://doi.org/10.1109/ICMLC.2008.4620536 (1/0/3)
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  (2008) Feng Guo(*), Xizhao Wang, Yan Li, A New Algorithm for Solving Convex Hull Problem and Its Application to Feature Selection, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, China, July 2008, Proceedings pp: 369-373, doi: https://doi.org/10.1109/ICMLC.2008.4620433 (4/3/5)
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  (2008) Xizhao Wang(*), Bo Wu, Yulin He, Xianghao Pei, NRMCS: Noise Removing Based on the MCS, Proceedings of the Seventh International Conference on Machine Learning and Cyberne tics, Kunming, China, July 2008, Proceedings pp: 89-93, doi: https://doi.org/10.1109/ICMLC.2008.4620384 (10/7/12)
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  (2008) Xizhao Wang(*), Junhai Zhai, Sufang Zhang, Fuzzy Decision Tree Based on the Important Degree of Fuzzy Attribute, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, China, July 2008, Proceedings pp: 511-516, doi: https://doi.org/10.1109/ICMLC.2008.4620458 (3/0/6)
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  (2008) Mingzhu Lu(*), C. L. Philip Chen, Jianbing Huo, Xizhao Wang, Optimization of combined kernel function for SVM based on large margin learning theory, In Proceedings of 2008 International Conference on Systems, Man, and Cybernetics, Singapore, October 2008, Proceedings pp: 353-358, doi: https://doi.org/10.1109/ICSMC.2008.4811301 (10/5/12)
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  (2008) Hongjie Xing(*), Xizhao Wang, Ruixian Zhu, Dan Wang, Application of kernel learning vector quantization to novelty detection, In Proceedings of 2008 International Conference on Systems, Man, and Cybernetics, Singapore, October 2008, Proceedings pp: 439-443, doi: https://doi.org/10.1109/ICSMC.2008.4811315 (1/0/1)
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  (2007) Degang Chen(*), Xizhao Wang, Suyun Zhao, Attribute reduction based on fuzzy rough sets, Proceedings of the International Conference on Rough Sets and Intelligent Systems Paradigms, Warsaw, Poland, June 2007, Rough Sets and Intelligent Systems Paradigms, Proceedings pp: 381-390, doi: https://doi.org/10.1007/978-3-540-73451-2_40 (18/0/29)
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  (2007) Weili Zhang(*), Xizhao Wang, Feature extractionand classification for human brain CT images, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, China, Augest 2007, Proceedings pp: 1155-1159, doi: https://doi.org/10.1109/ICMLC.2007.4370318 (0/10/39)
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  (2007) Xizhao Wang(*), Weixi Lin, Application of inductive learning in human brain CT image recognition, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, China, Augest 2007, Proceedings pp: 1667-1671, doi: https://doi.org/10.1109/ICMLC.2007.4370415 (1/0/4)
• [49]
  (2007) Xizhao Wang(*), Xiaoyan Liu, Yan Li and Chunguo Li, Norm-based localized generalization error model and its derivation for radial basis function neural networks, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, China, Augest 2007, Proceedings pp: 3623-3527, doi: https://doi.org/10.1109/ICMLC.2007.4370757 (0/0/1)
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  (2007) Xizhao Wang(*), Bin Wu, Jie Li, An improvement for localized generalization error model, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, China, August, 2007, Proceedings pp: 2901-2910, doi: https://doi.org/10.1109/ICMLC.2007.4370644 (1/0/1)
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  (2007) Xizhao Wang(*), Jianhui Yan, Ran Wang and Chunru Dong, A sample selection algorithm in fuzzy decision tree induction and its theoretical analyses, Proceedings of 2007 IEEE International Conference on Systems, Man and Cybernetics, Montreal, Canada, October 2007, Proceedings pp: 3621-3626, doi: https://doi.org/10.1109/ICSMC.2007.4413726 (10/0/14)
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  (2007) Xizhao Wang(*), Shuxia Lu and Ruixian Zhu, Solving SVM inverse problems based on clustering, Proceedings of 2007 IEEE International Conference on Systems, Man and Cybernetics, Montreal, Canada, October 2007, Proceedings pp: 3615-3620, doi: https://doi.org/10.1109/ICSMC.2007.4413725 (1/0/1)
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  (2007) Limei Feng(*), Xizhao Wang, Improving on symbolic learning system based on genetic algorithm, Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering, Chengdu, China, October 2007, Proceedings pp: 1132-1138, doi: https://doi.org/10.2991/iske.2007.183 (0/0/0)
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  (2007) Jinyan Sun(*), Xizhao Wang, A new method for constructing radial basis function neural networks, Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering, Chengdu, China, October 2007, Proceedings pp: 1240-1245, doi: https://doi.org/10.2991/iske.2007.182 (0/5/0)
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  (2006) Chenxiao Yang(*), Xizhao Wang and Ruixian Zhu, A strategy of merging branches based on margin enlargement of SVM in decision tree induction, Proceedings of 2006 IEEE International Conference on Systems, Man and Cybernetics, Taipei, China, October 2006, Proceedings pp: 824-828, doi: https://doi.org/10.1109/ICSMC.2006.384490 (2/0/2)
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  (2006) Jianbing Huo(*), Xizhao Wang, Mingzhu Lu and Junfen Chen, Induction of multi-stage decision tree, Proceedings of 2006 IEEE International Conference on Systems, Man and Cybernetics, Taipei, China, October 2006, Proceedings pp: 835-839, doi: https://doi.org/10.1109/ICSMC.2006.384492 (2/1/10)
• [41]
  (2006) Xizhao Wang(*) ,Xianghui Gao, A research on the relation between training ambiguity and generalization capability, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, China, August 2006, Proceedings pp:2008-2013, doi: https://doi.org/10.1109/ICMLC.2006.259133 (2/0/2)
• [40]
  (2006) Miao Wang(*), Xizhao Wang. A Research on Weight Acquisition of Weighted Fuzzy Production Rules Based on Genetic Algorithm. Proceedings of 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 August 2006, Proceedings pp: 2208-2211, doi: https://doi.org/10.1109/ICMLC.2006.258622 (1,0,3)
• [39]
  (2006) Xizhao Wang(*), Shuxia Lu. Improved fuzzy multicategory support vector machines classifier. Proceedings of 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 August 2006, Proceedings pp: 3585-3589, doi: https://doi.org/10.1109/ICMLC.2006.258575 (7,2,9)
• [38]
  (2006) Xizhao Wang(*), Mingzhu Lu, Jianbing Huo. Fault diagnosis of power transformer based on large margin learning classifier. Proceedings of 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 August 2006, Proceedings pp: 2886-2891, doi: https://doi.org/10.1109/ICMLC.2006.259075 (7,5,8)
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  (2006) Xizhao Wang(*), Feng Yang, Yan Li. A Disscussion on the Overlapping in Fuzzy Production Rule Reasoning. Proceedings of 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 August 2006, Proceedings pp: 4557-4562, doi: https://doi.org/10.1109/ICMLC.2006.258377 (0,0,2)
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  (2005) Xizhao Wang(*), Xuguang Wang, Jun Shen. The representation of interaction among fuzzy rules. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 3098-3103, doi: https://doi.org/10.1109/ICMLC.2005.1527474 (0,0,0)
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  (2005) Xizhao Wang(*), Jun Shen, Xuguang Wang. Using 2-additive fuzzy measure to represent the interaction among if-then rules. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 2797-2801, doi: https://doi.org/10.1109/ICMLC.2005.1527418 (0,2,1)
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  (2005) Xizhao Wang(*), Yan Ha, Degang Chen. On the reduction of fuzzy rough sets. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 3174-3178, doi: https://doi.org/10.1109/ICMLC.2005.1527489 (0,11,12)
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  (2005) Xizhao Wang(*), Sufang Zhang, Junhai Zhai. A nonlinear integral defined on partition of set and its fundamental properties. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 3092-3097, doi: https://doi.org/10.1109/ICMLC.2005.1527473 (0,0,0)
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  (2005) Xizhao Wang(*),Hui Zhang. An upper bound of input perturbation for RBFNNs sensitivity analysis. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 4704-4709, doi: https://doi.org/10.1109/ICMLC.2005.1527769 (0,0,3)
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  (2005) Xizhao Wang(*), Ying Xu. Multilevel weighted fuzzy reasoning with interaction. Proceedings of 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, 10-12 October 2005, Proceedings pp: 708-715, doi: https://doi.org/10.1109/ICSMC.2005.1571230 (3,3,4)
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  (2005) Xizhao Wang(*), Chunguo Li. A new definition of sensitivity for RBFNN and its applications to feature reduction. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 81-86, doi: https://doi.org/10.1007/11427391_12 (12,64,14)
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  (2005) Juan Sun(*), Xizhao Wang. An initial comparison on noise resisting between crisp and fuzzy decision trees. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 2545-2550, doi: https://doi.org/10.1109/ICMLC.2005.1527372 (0,3,13)
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  (2005) John W.T. Lee(*), Xizhao Wang, Jinfeng Wang. Finding reducts for ordinal decision tables. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 3143-3147, doi: https://doi.org/10.1109/icmlc.2005.1527483 (0,0,0)
• [27]
  (2004) Xizhao Wang(*), Chunru Dong, Daniel Yeung. A study on generalization capability of weighted fuzzy production rules with maximum entropy. Proceedings of 2004 IEEE International Conference on Systems, Man and Cybernetics, The Hague, Netherlands, 10-13 October 2004, Proceedings pp: 3181-3186, doi: https://doi.org/10.1109/ICSMC.2004.1400829 (1,0,1)
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  (2004) Xizhao Wang(*), Xiaojun Wang. A new methodology for determining fuzzy densities in the fusion model based on fuzzy integral. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26-29 August 2004, Proceedings pp: 2028-2031, doi: https://doi.org/10.1109/ICMLC.2004.1382128 (0,5,17)
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  (2004) Xizhao Wang(*), Xiaoying Lu, Feng Zhang. Feature selection based on fuzzy extension matrix for multi-class problem. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26-29 August 2004, Proceedings pp: 2032-2035, doi: https://doi.org/10.1109/ICMLC.2004.1382129 (0,1,2)
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  (2004) Xizhao Wang(*), Junfen Chen. Multiple neural networks fusion model based on croquet fuzzy integral. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26-29 August 2004, Proceedings pp: 2024-2027, doi: https://doi.org/10.1109/ICMLC.2004.1382127 (0,5,20)
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  (2004) Xizhao Wang(*), Huimin Feng. Nonnegative set functions in multiple classifier fusion. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26-29 August 2004, Proceedings pp: 2020-2023, doi: https://doi.org/10.1109/ICMLC.2004.1382126 (0,2,6)
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  (2004) Wing Ng(*), Daniel Yeung, Xizhao Wang, Ian Cloete. A study of the difference between partial derivative and stochastic neural network sensitivity analysis for applications in supervised pattern classification problems. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26-29 August 2004, Proceedings pp: 4283-4288, doi: https://doi.org/10.1109/ICMLC.2004.1384590 (0,5,15)
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  (2003) Yong Li(*), Xizhao Wang, Qiang Hua. Using BP-network to construct fuzzy decision tree with composite attributes. Proceedings of 2003 International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003, Proceedings pp: 1791-1795, doi: https://doi.org/10.1109/ICMLC.2003.1259787 (0,1,6)
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  (2003) Yan Li(*), Xizhao Wang, Minghu Ha. On-line multi-CBR agent dispatching. Proceedings of 2003 International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003, Proceedings pp: 2071-2075, doi: https://doi.org/10.1109/ICMLC.2003.1259845 (0,1,4)
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  (2003) Suyun Zhao(*), Xizhao Wang. A fuzzy model of rough sets. Proceedings of 2003 International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003, Proceedings pp: 1687-1691, doi: https://doi.org/10.1109/ICMLC.2003.1259768 (0,0,1)
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  (2003) Dazhong Liu(*), Xizhao Wang, J. W. T. Lee. Ordinal fuzzy sets and rough sets. Proceedings of 2003 International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003, Proceedings pp: 1438-1441, doi: https://doi.org/10.1109/ICMLC.2003.1259719 (0,1,1)
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  (2003) Qiang He(*), Xizhao Wang, Hongjie Xing. A fuzzy classification method based on support vector machine. Proceedings of 2003 International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003, Proceedings pp: 1237-1240, doi: https://doi.org/10.1109/ICMLC.2003.1259676 (0,0,0)
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  (2003) Qunfeng Zhang(*), Xizhao Wang, Jinghong Wang. A further study on simplification of decision tables. Proceedings of 2003 International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003, Proceedings pp: 1657-1661, doi: https://doi.org/10.1109/ICMLC.2003.1259762 (0,0,0)
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  (2003) Hua Li(*), Xizhao Wang, Yong Li. Using mutual information for selecting continuous-valued attribute in decision tree learning. Proceedings of 2003 International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003, Proceedings pp: 1496-1499, doi: https://doi.org/10.1109/ICMLC.2003.1259731 (0,0,2)
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  (2003) Shixin Zhao(*), Xizhao Wang. Core and reduction from mutual relation view and their fuzzy generalization. Proceedings of 2003 IEEE International Conference on Systems, Man and Cybernetics, Washington, DC, USA, 6-9 October 2003, Proceedings pp: 2611-2616, doi: https://doi.org/10.1109/ICSMC.2003.1244277 (0,2,4)
• [13]
  (2002) Yan Li(*) , Minghu Ha, Xizhao Wang. Principle and Design of Fuzzy Controller Based on Fuzzy Learning from Examples. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 1441-1446, doi: https://doi.org/10.1109/ICMLC.2002.1167445 (0,1,4)
• [12]
  (2002) Dongmei Huang(*), Xizhao Wang, Minghu Ha. The Optimization Problem of the Fuzzy Bi-Branches Decision Trees. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 1667-1668, doi: https://doi.org/10.1109/ICMLC.2002.1167496 (0,3,3)
• [11]
  (2002) Dazhong Liu(*), Xizhao Wang, John W.T. Lee. Correlation Based Generating Rules for Fuzzy Classification. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 1733-1736, doi: https://doi.org/10.1109/ICMLC.2002.1175333 (0,1,2)
• [10]
  (2002) Hongjie Xing(*), Xizhao Wang, Qiang He, Hongwei Yang. The Multistage Support Vector Machine. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 1815-1818, doi: https://doi.org/10.1109/ICMLC.2002.1175353 (0,0,2)
• [9]
  (2002) Xizhao Wang(*), Minghua Zhao, Daniel So Yeung. Parametric Sensitivity in Building Fuzzy Decision Trees: an Experimental Analysis. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 1819-1823, doi: https://doi.org/10.1109/ICMLC.2002.1175354 (0,0,1)
• [8]
  (2002) Xizhao Wang(*), Hongwei Yang, Minghua Zhao, Juan Sun. A Decision Tree Based on Hierarchical Decomposition. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 1824-1828, doi: https://doi.org/10.1109/ICMLC.2002.1175355 (0,0,4)
• [7]
  (2002) Lijuan Wang(*), Xizhao Wang, Minghu Ha, Yinshan Gu. Mining the Weights of Similarity Measure Through Learning. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 1837-1841, doi: https://doi.org/10.1109/ICMLC.2002.1175359 (0,0,2)
• [6]
  (2002) Daniel So Yeung(*), Juan Sun, Xizhao Wang. An Initial Comparison of Generalization-Capability between Crisp and Fuzzy Decision Trees. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 1846-1851, doi: https://doi.org/10.1109/ICMLC.2002.1175359 (0,2,12)
• [5]
  (2002) Shenshan Qiu(*), Eric C.C. Tsang, Daniel S. Yeung, Xizhao Wang. Energy Function Criterion for Discrete Hopfield-Type Neural Network with Delay. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4-5 November 2002, Proceedings pp: 2240-2244, doi: https://doi.org/10.1109/ICMLC.2002.1175438 (0,0,0)
• [4]
  (2001) Ruifeng Xu(*), D. S. Yeung, Xizhao Wang. Using neural network classifier in post-processing system for handwritten Chinese character recognition. Proceedings of 2001 IEEE International Conference on Systems, Man and Cybernetics, Tucson, AZ, USA, 07-10 October 2001, Proceedings pp: 1497-1502, doi: https://doi.org/10.1109/ICSMC.2001.973493 (0,0,7)
• [3]
  (2001) E. C. C. Tsang(*), D. S. Yeung, Xizhao Wang. Learning weights of fuzzy production rules by a max-min neural network. Proceedings of 2001 IEEE International Conference on Systems, Man and Cybernetics, Tucson, AZ, USA, 07-10 October 2001, Proceedings pp: 1485-1490, doi: https://doi.org/10.1109/ICSMC.2001.973493 (1,2,7)
• [2]
  (2000) Xizhao Wang(*), D. S. Yeung. Using fuzzy integral to modeling case based reasoning with feature interaction. Proceedings of 2000 IEEE International Conference on Systems, Man and Cybernetics, Nashville, TN, USA, 8-11 October 2000, Proceedings pp: 3660-3665, doi: https://doi.org/10.1109/ICSMC.2000.886578 (9,0,23)
• [1]
  (2000) D. S. Yeung(*), Xizhao Wang. Using a neuro-fuzzy technique to improve the clustering based on similarity. Proceedings of 2000 IEEE International Conference on Systems, Man and Cybernetics, Nashville, TN, USA, 8-11 October 2000, Proceedings pp: 3693-3698, doi: https://doi.org/10.1109/ICSMC.2000.886584 (1,0,9)

JMLC papers

• [8]
  JMLC Volume 12(1-12)2021
• [7]
  JMLC Volume 11(1-12)2020
• [6]
  JMLC Volume 10(1-12)2019
• [5]
  JMLC Volume 9(1-12) 2018
• [4]
  JMLC Volume 8(1-6) 2017
• [3]
  JMLC Volume 7(1-6) 2016
• [2]
  JMLC Volume 6(1-6) 2015
• [1]
  JMLC Volumes 1-5(2010-2014)

Reference

• [6]
  Learnability can be undecidable
• [5]
  Learning deep architectures for ai - Yoshua Bengio
• [4]
  A Theory of the Learnable - L.G.VALIANT
• [3]
  Hierarchical Models, Nested Models and Completely Random Measures - Michael I.Jordan
• [2]
  Restricted Boltzmann machine to determine the input weights for extreme learning machines - Andre G. C. Pacheco,Renato A. Krohling, Carlos A. S. da Silva
• [1]
  Why Does Unsupervised Pre-training Help Deep Learning - Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio

Group Meeting    (Template: Slide-Latex.zip)

• [51]
  Date: 28 December 2020
  Introduction to Quantum Machine Learning (Speaker: Prof. Daoyi Dong)
  Knowledge Graph (Speaker: Chao Liu, Si Cen, Xiaoying Zou, Guoquan Dai)
  
• [50]
  Date: 21 December 2020
  Research on Knowledge Granule Reduction and Hybrid Rule Extraction under Conceptual Cognitive Learning (Speaker: Yidong Lin)
  Exploration and construction of lightweight image restoration model (Speaker: Juncheng Li)
  Research Progress (Speaker: Di Wang)
  
• [49]
  Date: 14 December 2020
  Article analysis (Speaker: Dr. Zhongwu Xie)
  Article analysis (Speaker: Xinlei Zhou)
  
• [48]
  Date: 7 December 2020
  The concept of three decision-making and its application in machine learning (Speaker: Prof. Xionghua Li)
  Article analysis (Speaker: Dr. Hong Zhu)
  Article analysis (Speaker: Dr. Zhongwu Xie)
  Article analysis (Speaker: Xinlei Zhou)
  
• [47]
  Date: 30 November 2020
  Self-supervision (Speaker: Prof. Mingwen Shao)
  Selective Ensemble of Classifiers Trained on Selective Samples (Speaker: Dr. Han Liu)
  
• [46]
  Date: 23 November 2020
  Zeroing Neural Network: Model, Theory and Application (Speaker: Prof. Lin Xiao)
  Article analysis (Speaker: Dr. Salim Resvani)
  An Inconsistent-based active learning algorithms for mulit-label data (Speaker: Yu Yu)
  
• [45]
  Date: 9 November 2020
  Dynamic deep neural network (Speaker: Prof. Gao Huang)
  SUR model on Classification Problem (Speaker: Sudong Chen)
  Article analysis(Speaker: Dr. Hong Zhu)
  
• [44]
  Date: 2 November 2020
  Dynamic and local double-quantitative decision analysis and adaptive weighted decision fusion based on DTRS (Speaker: Dr. Yanting Guo)
  Analysis of Meta Features：an important dimension of meta-learning framework (Speaker: Xinlei Zhou)
  Dual VAEGAN: A Generative Model for Generalized Zero-Shot Learning (Speaker: Yuxuan Luo)
  Zero shot learning based on data augmentation (Speaker: Zhongwu Xie)
  
• [43]
  Date: 26 October 2020
  Some progress in adversarial machine learning (Speaker: Prof. Jun Zhu)
  Generalized adversarial examples: attacks and defences (Speaker: Haojing Shen)
  Research on the Relationship between Prediction Uncertainty and Adversarial Robustness (Speaker: Sihong Chen)
  The new idea of fuzzy min-max method (Speaker: Di Wang)
  
• [42]
  Date: 19 October 2020
  Deep Symbolic Learning: Numeric Encoding of Symbolic Features for Deep Models (Speaker: Dr. Han Liu, Chengwei Li)
  Fuzzy Missing Data Handling (Speaker: Hufsa Khan)
  
• [41]
  Date: 12 October 2020
  Data Value Mining under Privacy Protection: Challenges and Methods (Speaker: Dr. Jiahao Cao)
  
• [40]
  Date: 2 July 2020
  Impact of Uncertainty on Robustness in Adversarial Learning (Speaker: Haojing Shen, Sihong Chen)
  Seemingly Unrelated Regression (Speaker: Sudong Chen)
  
• [39]
  Date: 11 June 2020
  Uncertainty Evaluation (Speaker: Xinlei Zhou)
  Classifier Fusion based on fuzzy integrals (Speaker: Syeda Zoupash Zahra)
  Recent progress (Speaker: Zhongwu Xie)
  Cross Attention for Zero Shot Learning (Speaker: Yuxuan Luo)
  
• [38]
  Date: 26 December 2019
  Fine-tuning Modeling Method base on the Evolutionary Algorithm (Speaker: Shuyue Chen)
  Research on Deep Learning of Line and Slope Fine-Tuning Based on Point Cloud Data (Speaker: Lei Hu)
  A Data preprocessing method based on 1/2 similarity deviation (Speaker: Dasen Yan)
  Semisupervised fuzzy ART model (Speaker: Dr. Farhad Pourpanah)
  A summary at the end of 1st semester from 2019-2020 academic year and some arrangements for holiday (Speaker: Prof. Xizhao Wang)
  
• [37]
  Date: 19 December 2019
  A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning (Speaker: Zhongwu Xie)
  Fuzzy Monotonic K-Nearest Neighbor versus Monotonic Fuzzy K-Nearest Neighbor (Speaker: Hong zhu)
  
• [36]
  Date: 12 December 2019
  Intuitionistic fuzzy twin support vector machine for class imbalance learning:IFTSVM-CIL (Speaker: Salim Resvani)
  Research Progress Report (Speaker: Muhammed Jamshed Alam Patwary)
  How to Deal with the Tense of Verb in Scientific Papers (Speaker: Dr. Weipeng Cao)
  
• [35]
  Date: 28 November 2019
  How to Deal with the Tense of Verb in Scientific Papers (Speaker: Dr. Weipeng Cao)
  
• [34]
  Date: 6 November 2019
  SUR and classification (Speaker: Chengwei Li)
  Adversarial examples (Speaker: Haojing Shen)
  
• [33]
  Date: 23 October 2019
  How to find the correlative auxiliary dataset (Speaker: Zhongwu Xie)
  Fuzzy monotonic K-nearest neighbors (Speaker: Hong Zhu)
  
• [32]
  Date: 16 October 2019
  Analysis of relationship between uncertainty and misclassification rate (Speaker: Xinlei Zhou)
  Reformed Genetic Algorithm (Speaker: Zichao Zhang)
  Combination of Class Hierarchy and Zero-shot Learning model (Speaker: Yuxuan Luo)
  
• [31]
  Date: 15 October 2019
  Model-driven and data-driven in underlying visual tasks (Speaker: Prof. DeYu Meng) [ Variational Denoising Network]
• [30]
  Date: 18 September 2019
  How to find the correlative auxiliary dataset (Speaker: Zhongwu Xie)
  Generalized zero shot learning (Speaker: Yuxuan Luo)
  
• [29]
  Date: 11 September 2019
  A survey on imbalanced learning techniques (Speaker: Salim Resvani)
  
• [28]
  Date: 4 September 2019
  Inductive Bias (Speaker: Dr. Weipeng Cao)
  
• [27]
  Date: 19 June 2019
  Data enhancement methods including imbalanced learning and image enhance (Speaker: TianLun Zhang)
  
• [26]
  Date: 12 June 2019
  Semi-Supervised Learning - A Brief Survey (Speaker: Muhammed Jamshed Alam Patwary)
  
• [25]
  Date: 5 June 2019
  Research on Multiple Instance Active Learning Algorithm ased on Support Vector Machine (Speaker: Sudong Chen)
  Applications of Multi-Criteria Decision Making System in Multiple Instance Active Learning (Speaker: Sihong Chen)
  A Comparative Study of the Correlation between Stock Return Rate and Inflation Rate between China and the United States (Speaker: Chengwei Li)
  Learning Scene Functionality via Activity Prediction and Hallucination (Speaker: Haojing Shen)
  Group Theory-based Optimization Algorithm (GTOA)----based on Genetic Algorithm (Speaker: Zichao Zhang)
  
• [24]
  Date: 29 May 2019
  Research on Model Tree Algorithms Based on Network Structure (Speaker: XinLei Zhou)
  An Off-center Technique: Learning a Feature Transformation to Improve the Performance of Clustering and Classification (Speaker: Dasen Yan)
  Modeling of Metro Routing and Slope Adjustment Based on Evolutionary Algorithms (Speaker: Shuyue Chen / Lei Hu)
  
• [23]
  Date: 22 May 2019
  Big Data Dependency classification and Its Application in Stock Market Analysis (Speaker: Dr. Li Zhao)
  Effective Scientific Presentations (Speaker: Prof. Xizhao Wang)
  
• [22]
  Date: 15 May 2019
  An brief introduction to GAN (Speaker: Prof. Chunru Dong)
  
• [21]
  Date: 5 May 2019
  A Study on Model Optimization of Neural Networks with Random Weights (Speaker: Dr. Weipeng Cao)
  Report (Speaker: Yuxuan Luo)
  
• [20]
  Date: 24 April 2019
  Multi-swarm BSO for dynamic optimization (Speaker: Dr. Farhad Pourpanah)
  A survey of Class imbalanced learning methods (Speaker: Salim Resvani)
  
• [19]
  Date: 10 April 2019
  Tree-Based Space Partition and Merging Ensemble Learning Framework for Imbalanced Problems (Speaker: Salim Resvani)
  Weak supervision (Speaker: Prof. Xizhao Wang)
  
• [18]
  Date: 27 March 2019
  Use of sensor systems with spectral pattern recognition for food authentication (Speaker: Dr. Weiran Song)
  Report (Speaker: Dr. Farhad Pourpanah)
  
• [17]
  Date: 20 March 2019
  Sensitivity analysis of initial classifier accuracy in fuzziness based semi-supervised learning (Speaker: Dr. Muhammed Jamshed Alam Patwary)
  Genetic algorithm and Group Theory-based Optimization Algorithm (Speaker: Zichao Zhang)
  
• [16]
  Date: 13 March 2019
  Intuitionistic Fuzzy Twin Support Vector Machines for Imbalance Class Learning (Speaker: Salim Resvani)
  Learning a Feature Transformation to Improve Performance of Clustering and Classification (Speaker: Xinlei Zhou)
  
• [15]
  Date: 6 March 2019
  Deep meta-learning - learning to learn in the concept space (Speaker: Zhongwu Xie)
  Community detection in attributed social networks (Speaker: Prof. Xiaoou Li)
  
• [14]
  Date: 14 January 2019
  West extension design of line 9 (Speaker: Shuyue Chen)
  
• [13]
  Date: 24 December 2018
  Adaptive Resonance Theory (Speaker: Dr. Farhad Pourpanah)
  Brief introduction about Least Square Estimation Seemingly Uncorrelated Regression and Transfer Learning (Speaker: Dr. Li Zhao)
  
• [12]
  Date: 17 December 2018
  Binary Brain Storm Optimization in Objective Space based Feature Selection for Data Classification (Speaker: Dr. Farhad Pourpanah)
  
• [11]
  Date: 3 December 2018
  Improving Model Performance By Feature Weight Learning (Speaker: Dasen Yan)
  Using Meta-learning to recommend Multi-Layer Perceptron structure (Speaker: Shuyue Chen)
  
• [10]
  Date: 19 November 2018
  Convolution (Speaker: Prof. Xizhao Wang)
  The BP Algorithm on CNN (Speaker: Zhongwu Xie)
  Reinforcement Learning (Speaker: Xinlei Zhou)
  
• [9]
  Date: 5 November 2018
  Knapsack problem and its evolutionary algorithm (Speaker: Prof. Yichao He)
  The difference between CNN and MLP (Speaker: Yuxuan Luo)    [ reference: https://www.cnblogs.com/pinard/p/6494810.html ]
  
• [8]
  Date: 23 October 2018
  The difference between CNN and DL (Speaker: Yuxuan Luo & Zhongwu Xie)
  
• [7]
  Date: 15 October 2018
  Report(Speaker: Dr. Farhad Pourpanah)
  Sensitivity Analysis on Initial Classifier Accuracy in Fuzziness based Semi-supervised Learning(Speaker: Muhammed Jamshed Alam Patwary)
  An Intelligent Legal Aid System(Speaker: Dr. Weipeng Cao)
  Learning from Correlation(Speaker: Dr. Li Zhao)
  Weight Learning from Cost Matrix in Weighted Least Squares Model Based on Genetic Algorithm(Speaker: Dr. Hong Zhu)
  
• [6]
  Date: 8 October 2018
  How to select representative data to train SVM efficiently (Speaker: Prof. Xiaoou Li)
  Multi-label Learning(MLL) Classifier Chain (Speaker: Suhe Ye)
  Generative Adversarial Networks (Speaker: Mingde Wang)
  
• [5]
  Date: 24 September 2018
  Generative Adversarial Networks (Speaker: Mingde Wang)
  Research Summary and Prospect of ELM-Tree Model (Speaker: Xinlei Zhou)
  Depth OR Width (Speaker: Zhonwu Xie)
  
• [4]
  Date: 2 July 2018
  Semi-Supervised Learning (Speaker: Muhammed Jamshed Alam Patwary )
  Experimental report on deep learning of symbolic value data (Speaker: Lei Hu)
  Stage Summary and Outlook of ELMT project (Speaker: Dasen Yan)
  
• [3]
  Date: 25 June 2018
  Meta learning (Speaker: Dr. Weipeng Cao)
  Presentation about decision tree、neural network、Bayes classifier(Speaker: Yuxuan Luo)
  
• [2]
  Date: 11 June 2018
  Experimental report on deep learning of symbolic value data (Speaker: Shuyue Chen)
  Using Latex to Write a Scientific Paper(Speaker: Prof. Ran Wang)    [ annex:  A brief introduction of LATEX - Chinese  &  The Comprehensive LATEX Symbol List ]
  Convolutional Neural Network.pdf (Speaker: Zhongwu Xie)
  
• [1]
  Date: 4 June 2018
  Sensitivity Analysis of Computer Model (Speaker: Xinlei Zhou)
  Performance Analysis of Parallel Alogrithms (Speaker: Dasen Yan)
  Meta Learing (Speaker: Dr. Weipeng Cao)
  Experimental report on deep learning of symbolic value data (Speaker: Shuyue Chen)
  Presentation about Deep Learing (Speaker: Zhongwu Xie)
  COMPARING PREDICTIVE ACCURACY (Speaker: Dr. Li Zhao)