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.
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Nguyen, T.H., Foitong, S., Srinil, P., Pinngern, O. (2008). Towards Adapting XCS for Imbalance Problems. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_102
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DOI: https://doi.org/10.1007/978-3-540-89197-0_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89196-3
Online ISBN: 978-3-540-89197-0
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