Effects of distance between classes and training dataset size on imbalance datasets

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dc.contributor.authorHuy, Thach Nguyen
dc.contributor.authorFoitong, Sombut
dc.contributor.authorUdomthanapong, Sornchai
dc.contributor.authorPinngern, Ouen
dc.date.accessioned2026-08-06T09:58:21Z
dc.date.available2026-08-06T09:58:21Z
dc.date.issued2009-04-13
dc.description.abstractThis paper analyzes the effects of distance between classes and training datasets size to XCS classifier system on imbalanced datasets. Our purpose is to answer the question whether the loss of performance incurred by the classifier faced with class imbalance problems stems from the class imbalance per se or it can be explained in some other ways. The experiments from 250 artificial imbalanced datasets show that XCS can perform well in some imbalance domains if the training datasets size is large enough and the distance between classes is appropriate. Thus, it dose not seem fair to correlate imbalance datasets directly to the loss performance of XCS. Through this research, we also know what kinds of datasets are suitable for training XCS and dealing with class imbalances alone will not always help improve performance of classifiers. © 2009 American Institute of Physics.
dc.identifier.citationAip Conference Proceedings, 1089, 58-69, 2009
dc.identifier.doi10.1063/1.3078140
dc.identifier.issn0094243X
dc.identifier.other2-s2.0-63849331104
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/2512
dc.sourceAip Conference Proceedings
dc.subjectData mining
dc.subjectDistance between classes
dc.subjectImbalance dataset
dc.subjectXCS
dc.titleEffects of distance between classes and training dataset size on imbalance datasets
dc.typeConference Paper

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