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Combining boosting and evolutionary algorithms for learning of fuzzy classification rules
KTH.
2004 (English)In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 141, no 1, 47-58 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel boosting algorithm for genetic learning of fuzzy classification 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 classifier rule at a time. The boosting mechanism reduces the weight of those training instances that are classified correctly by the new rule. Therefore, the next rule generation cycle focuses on fuzzy rules that account for the currently uncovered or misclassified instances. The weight of a fuzzy rule reflects the relative strength the boosting algorithm assigns to the rule class when it aggregates the casted votes. The approach is compared with other classification algorithms for a number problem sets from the UCI repository.

Place, publisher, year, edition, pages
2004. Vol. 141, no 1, 47-58 p.
Keyword [en]
evolutionary algorithms, fuzzy classification rules
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-45907DOI: 10.1016/S0165-0114(03)00113-1ISI: 000187774100004Scopus ID: 2-s2.0-0347411204OAI: oai:DiVA.org:kth-45907DiVA: diva2:453143
Note
QC 20111101Available from: 2011-11-01 Created: 2011-11-01 Last updated: 2017-12-08Bibliographically approved

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  • apa
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  • fi-FI
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  • nn-NB
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  • Other locale
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Output format
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  • text
  • asciidoc
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