第二届模糊系统与数据挖掘国际学术会议
The 2nd International Conference on Fuzzy Systems and Data Mining
• 中文版    • English
特邀专家
Prof. Shu-Cherng Fang

North Carolina State University
Raleigh
USA

Biography: BS (National Tsing Hua University); MA (Johns Hopkins University); PhD (Northwestern University)

Shu-Cherng Fang holds the Walter Clark Chair Professorship in Industrial Engineering and Alumni Distinguished Graduate Professorship at North Carolina State University, USA. He is also Chair Professor and Team Leader of Chair Professors in Mathematical Sciences and Industrial Engineering at Tsinghua University, University Chair at Fudan University, Honorary University Professor of Northeast University, Honorary University Professor of Shanghai University, Graduate University Advisory Professor of the Chinese Academy of Sciences, Honorary University Chair Professor of National Chiao Tung University and Honorary IEEM Chair Professor of National Tsinghua University. Before joining NC State, Professor Fang was Senior Member of Research Staff at Western Electric Engineering Research Center, Supervisor at AT&T Bell Labs, and Department Manager at the Corporate Headquarters of AT&T Technologies. Professor Fang has published over two hundred refereed journal articles. He authored the books of Linear Optimization and Extensions: Theory and Algorithms (Prentice Hall 1993, with S. C. Puthenpura), Entropy Optimization and Mathematical Programming (Kluwer Academic 1997, with J.R. Rajasekera and H.-S. Tsao), and Linear Conic Optimization (Science Publisher 2013, with Wenxun Xing). He currently serves on the editorial board of 24 scientific journals, including Optimization, Journal of Global Optimization, Optimization Letters, Pacific Journal of Optimization, Journal of Management and Industrial Optimization, Journal of Operations and Logistics, International Journal of Operations Research, OR Transactions, Journal of Uncertainties, International Journal of Fuzzy Systems, Iranian Journal of Fuzzy Systems, Journal of Chinese Institute of Industrial Engineers and Journal of the Operations Research Society of China. He is also the Editor-in-Chief of Fuzzy Optimization and Decision Making. Professor Fang has won many awards and has been listed in several major biographic references. His research interests include Nonlinear Programming, Fuzzy Optimization and Decision Making, Soft Computing, and Logistics and Supply Chain Management.

Speech Title: Intuitionistic Fuzzy Set-based Semi-supervised Support Vector Machine for Binary Classification with Mislabeled Information
Abstract: Robust and fuzzy support vector machine models are commonly used to handle binary classification problems with noise and outliers. These models often suffer from the adverse effects caused by mislabeled training points and disregard the valuable position information carriedby such points. In this talk, we present a novel approach to address this issue. An intuitionistic fuzzy set is adopted for detecting “suspectable” mislabeled training points first. Then a semi-supervised support vector machine model is constructed by omitting suspected labels but utilizing their position information. This model can be reformulated as a non-convex quadratically constrained quadratic programming problem. We specially design a branch-and-bound algorithm with a new lower bound estimator to improve the accuracy and efficiency in computations. Some numerical tests are conducted to benchmark the performance of the proposed method vs. other support vector machine models. The results strongly support the superiority of the proposed approach.
Prof. Dr.-Ing. Jianhua Zhang

Intelligent Systems Group
Department of Automation
School of Information Science and Engingeering
East China University of Science and Technology
Shanghai 200237, P.R. China


Biography: BSc (Jiangxi University of Science and Technology, China); MSc (Beijing University of Technology, China); PhD (Ruhr-University Bochum, Germany)

Jianhua Zhang was born in Shaanxi, China in 1971. He received MSc degree in Control Theory and Applications from Beijing University of Technology, China, in April 1996 and PhD degree in Electrical Engineering under the supervision of Prof J.F. Böhme from Ruhr-Universität Bochum, Germany in Feb. 2005. Between 1999 and 2002 he was a Lecturer at Department of Automation, Beijing University of Technology. Afterwards he worked for three years as Guest Scientist and Scientific Coworker at Institute of Automation (Prof K. Janschek), Technische Universität Dresden and Signal Theory Group (Prof J.F. Böhme), Ruhr-Universität Bochum, both in Germany, respectively. More...

Speech Title: Prediction and Regulation of Operator Functional State Using Electrophysiological Data and Fuzzy Rule-based Models
Abstract: From the viewpoint of technical feasibility, economy and safety, one has realized that the implementation of fully-automated system to completely replace humans is impractical and human factors always exist in many complex sociotechnical systems. Therefore, the study of human-machine (HM) integrated systems has become an emerging research topic in the field of control systems and automation technology. In safety-critical HM systems, even a seemingly small/trivial accident may likely cause huge loss of lives or properties. The operator performance degradation or breakdown due to the deteriorated operator functional state (OFS) has been found to be one of main reasons of such accidents. More...
Prof. Xizhao Wang

Big Data Institute
College of Computer Science and Software Engineering
ShenZhen University
ShenZhen 518060, China


Biography: BS and MA (Hebei University); PhD (Harbin Institute of Technology)

Xizhao Wang received his PhD in computer science from Harbin Institute of Technology on September 1998. From 2000 to 2012 Dr. Wang served in Hebei University as a professor and the dean of school of Mathematics and Computer Sciences. From 2013 to now Dr. Wang worked as a professor in Big Data Institute of ShenZhen University since 2013. Prof. Wang’s major research interests include uncertainty modeling and machine learning for big data. Prof. Wang has edited 6+ special issues and published 3 monographs, 2 textbooks, and 150+ peer-reviewed research papers. By the Google scholar, the total number of citations is over 3000 and the maximum number of citation for a single paper is over 200. The H-index is 25 up to March 2015. Prof. Wang is on the list of Elsevier 2015 most cited Chinese authors. As a Principle Investigator (PI) or co-PI, Prof. Wang's has completed 30+ research projects. Prof. Wang is an IEEE Fellow, the previous BoG member of IEEE SMC society, the chair of IEEE SMC Technical Committee on Computational Intelligence, and the Chief Editor of Machine Learning and Cybernetics Journal.

Speech Title: Learning from Uncertainty for Big Data
Abstract: Big data refers to the datasets that are so large that conventional database management and data analysis tools are insufficient to work with them. Big data has become a bigger-than-ever problem with the quick developments of data collection and storage technologies. Model simplification is one of the most popular approaches to big data processing. After a brief tutorial of the existing techniques of processing big data, this talk will present some key issues of learning from big data with uncertainty, focusing on the impact of handling uncertainty on the model simplification. It shows that the representation, measure, and handling of the uncertainty have a significant influence on the performance of learning from big data. Some new advances in our Big Data Institute regarding the research on big data analysis and its applications to different domains are briefly introduced.
Prof. C. L. Philip Chen

Dean and Chair Professor
University of Macau
Macau
China

Biography: BS (National Taipei University of Technology); MA (The University of Michigan); PhD (Purdue University)

C. L. Philip Chen (S’88–M’88–SM’94–F’07) received his M.S. degree in electrical engineering from the University of Michigan, Ann Arbor, in 1985 and the Ph.D. degree in electrical engineering from Purdue University, West Lafayette, IN, in 1988. He is currently the Dean of the Faculty of Science and Technology, University of Macau, Macau, China and a Chair Professor of the Department of Computer and Information Science after having worked at U.S. for 23 years as a tenured professor, as a department head and associate dean in two different universities. Dr. Chen’s research areas are in systems, cybernetics and computational intelligence. He is a Fellow of the IEEE and AAAS. He was the President of IEEE Systems, Man, and Cybernetics Society (SMCS) (2012-2013). Currently, he is the Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-), and is an Associate Editor of IEEE Trans on Fuzzy Systems, IEEE Trans on Cybernetics, IEEE/CAA Automatica Sinica, and several IEEE Transactions. He is the Chair of TC 9.1 Economic and Business Systems of IFAC. He is also a Fellow of CAA and Fellow of HKIE and an Academician of International Academy of Systems and Cybernetics Science (IASCYS). In addition, he is an ABET (Accreditation Board of Engineering and Technology Education, USA) Program Evaluator for Computer Engineering, Electrical Engineering, and Software Engineering programs.

Speech Title: A Fuzzy Restricted Boltzmann Machine: An Approach to Enhance Deep Learning
Abstract: In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. In particular, deep belief networks and deep Boltzmann machines are formed by stacking RBMs layer by layer and optionally fine-tuning the resulted deep networks with a backpropagation algorithm. RBMs have been widely applied in diverse fields, such as image recognition and classification, dimensionality reduction, feature learning, and collaborative filtering, etc. This talk will introduce a Fuzzy Restricted Boltzmann Machine (FRBM) that is established by replacing real-valued weights and bias terms with symmetric triangular fuzzy numbers (STFNs) or Gaussian fuzzy numbers and corresponding learning algorithms. A theorem is concluded that all FRBMs with symmetric fuzzy numbers will have identical learning algorithm to that of FRBMs with STFNs. Experiments results in MNIST handwriting recognition and Bar-and-Stripes benchmark indicate that the proposed FRBMs significantly outperform RBMs in learning accuracy and generalization ability, especially when encountering unlearned samples and recovering incomplete images.
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Invited Speaker
Prof. Lazim Abdullah

School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia

Biography: Lazim Abdullah is a professor of computational mathematics at the School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu. He holds a B.Sc (Hons) in Mathematics from the University of Malaya, Kuala Lumpur in June 1984 and the M.Ed in Mathematics Education from University Sains Malaysia, Penang in 1999. He received his Ph.D. from the Universiti Malaysia Terengganu, (Information Technology Development) in 2004. More...

Speech Title: A New Integrated SAW-TOPSIS based on Interval Type-2 Fuzzy Sets for Decision Making
Abstract: Uncertainty and fuzziness of the real world problem could be represented by interval type-2 fuzzy sets where additional degrees of flexibility in decision making environment are presumed. This paper aims to propose an integrated method of interval type-2 fuzzy simple additive weighting (IT2 FSAW) and interval type-2 fuzzy technique for order preference by similarly to ideal solution (IT2 FTOPSIS). The IT2 FSAW is used to determine weight for each criterion, while IT2 FTOPSIS method is used to obtain the final ranking for the attributes. A numerical example is used to illustrate the proposed method. In essence, the integrated method is equipped with interval type-2 fuzzy sets in contrast to type-1 fuzzy sets.


Dr. Wenwu Wang

Centre for Vision Speech and Signal Processing, Department of Electronic Engineering, University of Surrey, UK

Biography: Wenwu Wang (M’02–SM’11) received the B.Sc. degree in automatic control, M.E. degree in control science and control engineering, and Ph.D. degree in navigation guidance and control from Harbin Engineering University, Harbin, China, in 1997, 2000, and 2002, respectively. He joined Kings College, London, U.K., in May 2002, as a Postdoctoral Research Associate and transferred to Cardiff University, Cardiff, U.K., in January 2004. More...

Speech Title: Sparse Analysis Model Based Dictionary Learning and Signal Reconstruction
Abstract: Sparse representation has been studied extensively in the past decade in a variety of applications, such as denoising, source separation and classification. Earlier effort has been focused on the well-known synthesis model, where a signal is decomposed as a linear combination of a few atoms of a dictionary. However, the analysis model, a counterpart of the synthesis model, has not received much attention until recent years. The analysis model takes a different viewpoint to sparse representation, and it assumes that the product of an analysis dictionary and a signal is sparse. Compared with the synthesis model, this model tends to be more expressive to represent signals, as a much richer union of subspaces can be described. This talk focuses on the analysis model and aims to discuss the two main challenges: analysis dictionary learning (ADL) and signal reconstruction. More...


Prof. Wenying Feng

Department of Computing & Information Systems and Department of Mathematics, Trent University, Ontario, Canada

Biography: Dr. Wenying Feng is a Professor in the Department of Computing & Information Systems and Department of Mathematics at Trent University, Ontario, Canada, and an Adjunct Professor at the School of Computing, Queen’s University, Canada. She received her Ph.D. in Mathematics from the University of Glasgow in 1997 and has published more than 90 articles in journals and conference proceedings covering areas of both Mathematics and Computer Science. More...

Speech Title: Computational Modelling with Real World Applications
Abstract: In this talk, I will present two computational models for machine learning and data mining with real-world applications. The first model is on deep learning neural network and is developed by introducing a new activation function that offers higher gradients and faster learning rate. Moreover, to simplify the network structure, a sparsity function is applied to the hidden layer of the network. A technique that swaps the activation functions of fully trained network to logistic function is introduced to enhance system performance.
The second model applies data mining techniques and is applied to detect coalition attacks, which is one of the most common types ofattacks in the industry of online advertising. In development of new algorithms, we attempt to mitigatethe problem of frauds by proposing a hybrid framework that detects thecoalition attacks based on multiple metrics. We also articulate the theoreticalbasis for these metrics to be integrated into the hybrid framework. Furthermore,we instance the framework with two metrics and develop a detection system that identifies the coalition attacks from two distinguish perspectives.


The 2nd International Conference on Fuzzy Systems and Data Mining (FSDM 2016)
Conference Secretary: Senlin Yan
Email: fsdm@fsdmconf.org   Tel: +86-18040526485