Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system

Jaume Bacardit, Josep Maria Garrell

    Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

    42 Cites (Scopus)

    Resum

    Bloat control and generalization pressure are very important issues in the design of Pittsburgh Approach Learning Classifier Systems (LCS), in order to achieve simple and accurate solutions in a reasonable time. In this paper we propose a method to achieve these objectives based on the Minimum Description Length (MDL) principle. This principle is a metric which combines in a smart way the accuracy and the complexity of a theory (rule set , instance set, etc.). An extensive comparison with our previous generalization pressure method across several domains and using two knowledge representations has been done. The test show that the MDL based size control method is a good and robust choice.

    Idioma originalAnglès
    Títol de la publicacióLearning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers
    EditorSpringer Verlag
    Pàgines59-79
    Nombre de pàgines21
    ISBN (imprès)9783540712305
    DOIs
    Estat de la publicacióPublicada - 2007

    Sèrie de publicacions

    NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volum4399 LNAI
    ISSN (imprès)0302-9743
    ISSN (electrònic)1611-3349

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