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Subject: search.filters.subject.Imbalance problem

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    Cost-sensitive XCS classifier system addressing imbalance problems
    (2008-12-01)
    Thach, Nguyen Huy
    ;
    Rojanavasu, Porntep
    ;
    Pinngern, Ouen
    The class imbalance problem has been recognized as a crucial problem in machine learning and data mining. Learning systems tend to be biased towards the majority class and thus have poor generalization for the minority class instances. This paper analyses the imbalance problem in accuracy-based learning classifier systems. In particular, we propose a novel approach based on XCS classifier system and cost-sensitive learning. In our approach, the reward value of correctly identifying the positive (rare) class outweighs the value of correctly identifying the common class. This research provides guidelines to set reward base on the dataset imbalance ratio and a method to calculate reward online base on the information collected by XCS during training is also proposed. Experimental results on synthetic and real-life datasets show that, with appropriate reward settings, XCS is robust to class imbalances. © 2008 IEEE.
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    Towards adapting XCS for imbalance problems
    (2008-12-01)
    Nguyen, Thach Huy
    ;
    Foitong, Sombut
    ;
    Srinil, Phaitoon
    ;
    Pinngern, Ouen
    The class imbalance problem has been recognized as a crucial problem in machine learning and data mining. Learning systems tend to be biased towards the majority class and thus have poor performance in classifying the minority class instances. This paper analyzes the imbalance problem in accuracy-based learning classifier system XCS. XCS has shown excellent performance on some data mining tasks, but as other classifiers, it also performs poorly on imbalance data problems. We analyze XCS's behavior on various imbalance levels and propose an appropriate parameter tuning to improve performance of the system. Particularly, XCS is adapted to eliminate over-general classifiers and protect accurate classifiers of minority class. Experimental results in Boolean function problems show that, with proposal adaptations, XCS is robust to class imbalance. © 2008 Springer Berlin Heidelberg.