NDLI: Comparison of Search Ability between Genetic Fuzzy Rule Selection and Fuzzy Genetics-Based Machine Learning

Content Provider	IEEE Xplore Digital Library
Author	Nojima, Y. Ishibuchi, H. Kuwajima, I.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci. & Intelligent Syst., Osaka Prefecture Univ. (Nojima, Y.; Ishibuchi, H.; Kuwajima, I.)
Abstract	We developed two GA-based schemes for the design of fuzzy rule-based classification systems. One is genetic rule selection and the other is genetics-based machine learning (GBML). In our genetic rule selection scheme, first a large number of promising fuzzy rules are extracted from numerical data in a heuristic manner as candidate rules. Then a genetic algorithm is used to select a small number of fuzzy rules. A rule set is represented by a binary string whose length is equal to the number of candidate rules. On the other hand, a fuzzy rule is denoted by its antecedent fuzzy sets as an integer substring in our GBML scheme. A rule set is represented by a concatenated integer string. In this paper, we compare these two schemes in terms of their search ability to efficiently find compact fuzzy rule-based classification systems with high accuracy. The main difference between these two schemes is that GBML has a huge search space consisting of all combinations of possible fuzzy rules while genetic rule selection has a much smaller search space with only candidate rules
Sponsorship	IEEE CIS
Starting Page	125
Ending Page	130
File Size	6924053
Page Count	6
File Format	PDF
ISBN	0780397185
DOI	10.1109/ISEFS.2006.251148
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-07
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Fuzzy systems Fuzzy sets Genetic algorithms Data mining Algorithm design and analysis Concatenated codes Knowledge based systems Genetic mutations System testing
Content Type	Text
Resource Type	Article

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