Towards adapting XCS for imbalance problems

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dc.contributor.authorNguyen, Thach Huy
dc.contributor.authorFoitong, Sombut
dc.contributor.authorSrinil, Phaitoon
dc.contributor.authorPinngern, Ouen
dc.date.accessioned2026-08-06T09:57:36Z
dc.date.available2026-08-06T09:57:36Z
dc.date.issued2008-12-01
dc.description.abstractThe 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.
dc.identifier.citationLecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5351 LNAI, 1028-1033, 2008
dc.identifier.doi10.1007/978-3-540-89197-0_102
dc.identifier.issn03029743
dc.identifier.other2-s2.0-58349092406
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/2301
dc.sourceLecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
dc.subjectClassification
dc.subjectGenetic algorithm
dc.subjectImbalance problem
dc.subjectLearning classifier system
dc.subjectXCS
dc.titleTowards adapting XCS for imbalance problems
dc.typeConference Paper

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