NDLI: A hybrid fuzzy GBML algorithm for designing compact fuzzy rule-based classification systems

Content Provider	IEEE Xplore Digital Library
Author	Ishibuchi, H. Nakashima, T. Kuroda, T.
Copyright Year	2000
Description	Author affiliation: Ind. Eng., Osaka Prefecture Univ., Japan (Ishibuchi, H.)
Abstract	We propose a hybrid algorithm of fuzzy versions of two genetics-based machine learning approaches: Michigan and Pittsburgh approaches. First, we examine the performance of each approach by computer simulations on commonly used data sets. Simulation results clearly demonstrate that each approach has its own advantages and disadvantages. While the Michigan approach has high search ability to efficiently find good fuzzy rules in large search spaces for high-dimensional pattern classification problems, it can not directly optimize fuzzy rule-based systems. On the other hand, the Pittsburgh approach can directly optimize fuzzy rule-based systems while its search ability to find good fuzzy rules is not high. Then we combine these two approaches into a single hybrid algorithm. Our hybrid algorithm is based on the Pittsburgh approach where a set of fuzzy rules is coded as a string. The Michigan approach is used as a mutation operation in our hybrid algorithm for partially modifying each string by generating new rules from existing good rules. In this manner, our hybrid algorithm utilizes the advantages of the two approaches.
Starting Page	706
Ending Page	711
File Size	548170
Page Count	6
File Format	PDF
ISBN	0780358775
ISSN	10987584
DOI	10.1109/FUZZY.2000.839118
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-05-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Fuzzy systems Machine learning algorithms Knowledge based systems Machine learning Computer simulation Computational modeling Pattern classification Fuzzy sets Genetic mutations
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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