Title:Improve neuro-fuzzy learning by attribute reduction Authors: Chang, Fengming M. (1); Chan, Chien-Chung (2) Author affiliation:(1) Department of Information Science and
Applications, Asia University, Wufeng, Taichung 41354, Taiwan; (2) Department of Computer Science, University of Akron, Akron, OH 44325-4003, United States
Corresponding author:Chang, F. M.
Source title:Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
Abbreviated source title:Annu Conf North Am Fuzzy Inf Process Soc NAFIPS
Monograph title:2008 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2008
Publication year:2008 Article number:4531208 Language:English
Document type:Conference article (CA)
Conference name:2008 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2008
Conference date:May 19, 2008 - May 22, 2008
Conference location:New York City, NY, United states Conference code:73325
Publisher:Institute of Electrical and Electronics Engineers Inc., 445 Hoes Lane / P.O. Box 1331, Piscataway, NJ 08855-1331, United States
Abstract:Neuro-fuzzy learning is a combination of neural networks and fuzzy systems to learn fuzzy rules from examples. One of the popular tools for neuro-fuzzy learning is the Adaptive Network based Fuzzy Inference Systems (ANFIS) introduced by Jang. It is observed from our past experiments that data sets with more than six
attributes (features) may present a challenge to ANFIS learning.
Rough set theory introduced by Pawlak has been shown as an effective tool for data reduction. This paper studied how ANFIS
learning may benefit from using rough set tools for data reduction.
Empirical results show that ANFIS learning from reduced data sets usually has better prediction accuracies and faster learning time.
Number of references:23 Main heading:Data reduction
Controlled terms: Adaptive systems - Artificial intelligence - Computer networks - Education - Fuzzy inference - Fuzzy logic - Fuzzy neural networks - Fuzzy sets - Fuzzy systems - Neural networks - Set theory - Statistics
Uncontrolled terms:Annual meetings - Neuro-fuzzy learning - Rough sets
Classification code:723.4 Artificial Intelligence - 723.4.1 Expert Systems - 731.1 Control Systems - 961 Systems Science - 901.2 Education - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 922.2 Mathematical Statistics - 921
Mathematics - 723.2 Data Processing and Image Processing - 723 Computer Software, Data Handling and Applications - 461.1
Biomedical Engineering - 716 Telecommunication; Radar, Radio and Television - 717 Optical Communication - 718 Telephone Systems and Related Technologies; Line Communications - 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory - 722 Computer Systems and Equipment
Compilation and indexing terms, Copyright 2009 Elsevier Inc.