NDLI: A hybrid fuzzy genetics-based machine learning algorithm: hybridization of Michigan approach and Pittsburgh approach

Content Provider	IEEE Xplore Digital Library
Author	Ishibuchi, H. Nakashima, T. Kuroda, T.
Copyright Year	1999
Description	Author affiliation: Dept. of Ind. Eng., Osaka Prefecture Univ., Japan (Ishibuchi, H.)
Abstract	In this paper, we propose a hybrid genetics-based machine learning algorithm for designing a linguistic classification system that consists of a small number of fuzzy if-then rules with clear linguistic interpretation. Our task is to generate a small number of fuzzy if-then rules from numerical data for a high-dimensional pattern classification problem. We assume that a set of linguistic values is given for each attribute of the pattern classification problem by human experts. Thus our task is described as finding a small number of combinations of linguistic values, each of which is used as the antecedent part of a fuzzy if-then rule. While this task seems to be simple at a glance, it is very difficult especially in the case of high-dimensional problems because the number of possible combinations of antecedent linguistic values exponentially increases with the dimensionality of problems. That is, the search space for high-dimensional problems is terribly huge. In our approach, an individual in genetic algorithms is a set of fuzzy if-then rules. Each rule is coded as a string by its antecedent linguistic values. Thus an individual (i.e., a rule set) is denoted by a concatenated string. The fitness of a rule set, which is defined by its classification performance, is used in a selection operation. New rule sets are generated by a crossover operation from selected rule sets. A mutation operation modifies a part of each rule set generated by the selection and crossover. As a mutation operation, we use a rule generation mechanism of the Michigan approach.
Starting Page	296
Ending Page	301
File Size	549679
Page Count	6
File Format	PDF
ISBN	0780357310
ISSN	1062922X
DOI	10.1109/ICSMC.1999.814106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-10-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Fuzzy systems Fuzzy sets Knowledge based systems Humans Genetic algorithms Algorithm design and analysis Pattern classification Genetic mutations Industrial engineering
Content Type	Text
Resource Type	Article

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