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    Human-like rule optimization for continuous domains

    116229_9785_PUB-CBS-EEB-MC-47025.pdf (686.2Kb)
    Access Status
    Open access
    Authors
    Hadzic, Fedja
    Dillon, Tharam S.
    Date
    2008
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Hadzic, Fedja and Dillon, Tharam. 2008. Human-like rule optimization for continuous domains, in Fred, A. and Filipe. J. and Gamboa , H.(ed), Biomedical Engineering Systems and Technologies. pp. 330-343. Heidelberg, Germany: Springer.
    Source Title
    Biomedical Engineering Systems and Technologies
    DOI
    10.1007/978-3-540-92219-3_25
    ISBN
    9783540922186
    Faculty
    Curtin Business School
    School of Information Systems
    School
    Centre for Extended Enterprises and Business Intelligence
    Remarks

    The original publication is available at www.springerlink.com

    URI
    http://hdl.handle.net/20.500.11937/42310
    Collection
    • Curtin Research Publications
    Abstract

    When using machine learning techniques for data mining purposes one of the main requirements is that the learned rule set is represented in a comprehensible form. Simpler rules are preferred as they are expected to perform better on unseen data. At the same time the rules should be specific enough so that the misclassification rate is kept to a minimum. In this paper we present a rule optimizing technique motivated by the psychological studies of human concept learning. The technique allows for reasoning to happen at both higher levels of abstraction and lower level of detail in order to optimize the rule set. Information stored at the higher level allows for optimizing processes such as rule splitting, merging and deleting, while the information stored at the lower level allows for determining the attribute relevance for a particular rule. The attributes detected as irrelevant can be removed and the ones previously detected as irrelevant can be reintroduced if necessary. The method is evaluated on the rules extracted from publicly available real world datasets using different classifiers, and the results demonstrate the effectiveness of the presented rule optimizing technique.

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