(Free) DLP on Making your Fuzzy Systems a Little More Comprehensible by Prof. Nikhil Pal, IEEE Fellow

IEEE Computational Intelligence Society (CIS) Malaysia section would like to invite all IEEE members to CIS Distinguished Lecturer Program (DLP) by Prof. Nikhil Pal, IEEE Fellow from the Indian Statistical Institute, India.

The details of this event:

Date: 01 November 2016 (Tuesday)

Time: 1000-1200

Venue: Faculty of Applied Sciences and Computing, Tunku Abdul Rahman University College, Kuala Lumpur.

Admission: FREE, please register at Registration of Distinguished Lecture Program

Title: Making your Fuzzy Systems a Little More Comprehensible

Abstract:

Fuzzy systems are often advocated for its interpretability. But when we extract fuzzy rules from data, we may lose it. There are many methods for generation of fuzzy rules, which may extract useful (in terms of accuracy) rules for classification and function approximation.  Automatic extraction of fuzzy rules from data raises many important issues that must be addressed, particularly if we want to exploit the benefits of comprehensibility of fuzzy systems. Some rules may be conflicting in nature, some rules may be redundant, while the biggest difficulty in understanding a fuzzy system arises if the rules involve even a moderately large number of attributes. These issues are related to dimensionality reduction/structure identification and assessment of conflict and redundancy in a rule base. In this talk, first we shall present an “interesting” approach to deal with some of these issues in an integrated manner.  A unique attribute of this approach is that it can exploit subtle non-linear interactions between features, the problem (that we intend to solve), and the tool (that is used to solve the problem). The formulation is adapted to all three types of fuzzy systems: classification systems, Mamdani type systems and Takagi-Sugeno type systems. This approach can deal with necessary features, indifferent features, and derogatory features in an appropriate manner but may not control the use of redundant features. So, we shall further generalize the approach to deal with the level of redundancy in the selected features.   Finally, we shall discuss a way to assess the level of conflict in a fuzzy rule base.

Biography:

Nikhil R. Pal (www.isical.ac.in/~nikhil) is an INAE Chair Professor in the Electronics and Communication Sciences Unit of the Indian Statistical Institute. His current research interest includes bioinformatics, brain science, fuzzy logic, neural networks, machine learning, and data mining. He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems (January 2005-December 2010). He has served/been serving on the editorial /advisory board/ steering committee of several journals including the International Journal of Approximate Reasoning, Applied Soft Computing, Fuzzy Sets and Systems, Fuzzy Information and Engineering : An International Journal, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Systems Man and Cybernetics B (currently IEEE Transactions on Cybernetics).

 

He is a recipient of the 2015 Fuzzy Systems Pioneer Award. He has given many plenary/keynote speeches in different premier international conferences in the area of computational intelligence. He has served as the General Chair, Program Chair, and co-Program chair of several conferences. He was a Distinguished Lecturer of the IEEE Computational Intelligence Society (CIS) and was a member of the Administrative Committee of the IEEE CIS. At present he is the Vice President for Publications of the IEEE CIS.

 

He is a Fellow of the National Academy of Sciences, India; the Indian National Academy of Engineering; the Indian National Science Academy, the International Fuzzy Systems Association (IFSA), and IEEE, USA.

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