Abstract
Several works point out class imbalance as an obstacle on applying machine learning algorithms to real world domains. However, in some cases, learning algorithms perform well on several imbalanced domains. Thus, it does not seem fair to directly correlate class imbalance to the loss of performance of learning algorithms. In this work, we develop a systematic study aiming to question whether class imbalances are truly to blame for the loss of performance of learning systems or whether the class imbalances are not a problem by themselves. Our experiments suggest that the problem is not directly caused by class imbalances, but is also related to the degree of overlapping among the classes.
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Prati, R.C., Batista, G.E.A.P.A., Monard, M.C. (2004). Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_32
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DOI: https://doi.org/10.1007/978-3-540-24694-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
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