Towards Adapting XCS for Imbalance Problems

Authors:
Thach Huy Nguyen;Sombut Foitong;Phaitoon Srinil;Ouen Pinngern
Affiliations:
Department of Computer Engineering, Faculty of Engineering, Research Center for Communication and Information Technology (ReCCIT), King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thail ...;Department of Computer Engineering, Faculty of Engineering, Research Center for Communication and Information Technology (ReCCIT), King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thail ...;Department of Computer Engineering, Faculty of Engineering, Research Center for Communication and Information Technology (ReCCIT), King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thail ...;Department of Computer Engineering, Faculty of Engineering, Research Center for Communication and Information Technology (ReCCIT), King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thail ...
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

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.