Class imbalance problem in UCS classifier system: Fitness adaptation

Albert Orriols, Ester Bernadĺ-Mansilla

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

    37 Cites (Scopus)

    Resum

    The class imbalance problem has been said to challenge the performance of concept learning systems. Learning systems tend to be biased towards the majority class, and thus have poor generalization for the minority class instances. We analyze the class imbalance problem in learning classifier systems based on genetic algorithms. In particular we study UCS, a rule-based classifier system which learns under a supervised learning scheme. We analyze UCS on an artificial domain with varying imbalance levels. We find UCS fairly sensitive to high levels of class imbalance, to the degree that UCS tends to evolve a simple model of the feature space classified according to the majority class. We analyze strategies for dealing with class imbalances, and find fitness adaptation based on class-sensitive accuracy a useful tool for alleviating the effects of class imbalances.

    Idioma originalAnglès
    Títol de la publicació2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
    Pàgines604-611
    Nombre de pàgines8
    Estat de la publicacióPublicada - 2005
    Esdeveniment2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom
    Durada: 2 de set. 20055 de set. 2005

    Sèrie de publicacions

    Nom2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
    Volum1

    Conferència

    Conferència2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
    País/TerritoriUnited Kingdom
    CiutatEdinburgh, Scotland
    Període2/09/055/09/05

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