The class imbalance problem in UCS classifier system: A preliminary study

Albert Orriols-Puig, Ester Bernadó-Mansilla

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    7 Cites (Scopus)


    The class imbalance problem has been said recently to hinder the performance of learning systems. In fact, many of them are designed with the assumption of well-balanced datasets. But this commitment is not always true, since it is very common to find higher presence of one of the classes in real classification problems. The aim of this paper is to make a preliminary analysis on the effect of the class imbalance problem in learning classifier systems. Particularly we focus our study on UCS, a supervised version of XCS classifier system. We analyze UCS's behavior on unbalanced dataseis and find that UCS is sensitive to high levels of class imbalance. We study strategies for dealing with class imbalances, acting either at the sampling level or at the classifier system's level.

    Idioma originalAnglès
    Títol de la publicacióLearning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers
    EditorSpringer Verlag
    Nombre de pàgines20
    ISBN (imprès)9783540712305
    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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