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Combining boosting and evolutionary algorithms for learning of fuzzy classification rules

โœ Scribed by Frank Hoffmann

Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
247 KB
Volume
141
Category
Article
ISSN
0165-0114

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โœฆ Synopsis

This paper presents a novel boosting algorithm for genetic learning of fuzzy classiรฟcation rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classiรฟer rule at a time. The boosting mechanism reduces the weight of those training instances that are classiรฟed correctly by the new rule. Therefore, the next rule generation cycle focuses on fuzzy rules that account for the currently uncovered or misclassiรฟed instances. The weight of a fuzzy rule re ects the relative strength the boosting algorithm assigns to the rule class when it aggregates the casted votes. The approach is compared with other classiรฟcation algorithms for a number problem sets from the UCI repository.

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