Classification rule induction based on relevant, irredundant attributes and rule expansion

George Lashkia, Laurence Anthony, Hiroyasu Koshimizu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper we focus on the induction of classification rules from examples. Conventional algorithms fail in discovering effective knowledge when the database contains irrelevant information. We present a new rule extraction method, RGT, which tackles this problem by employing only relevant and irredundant attributes. Simplicity of rules is also our major concern. In order to create only simple rules, we estimate the purity of patterns and propose a rule merging and expending procedures. In this paper, we describe the methodology for the RGT algorithm, discuss its properties, and compare it with conventional methods.

    Original languageEnglish
    Title of host publicationWMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
    Pages191-196
    Number of pages6
    Volume8
    Publication statusPublished - 2005
    Event9th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2005 - Orlando, FL
    Duration: 2005 Jul 102005 Jul 13

    Other

    Other9th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2005
    CityOrlando, FL
    Period05/7/1005/7/13

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    Keywords

    • Classification rules
    • Inductive learning
    • Prime test

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Networks and Communications
    • Information Systems

    Cite this

    Lashkia, G., Anthony, L., & Koshimizu, H. (2005). Classification rule induction based on relevant, irredundant attributes and rule expansion. In WMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings (Vol. 8, pp. 191-196)