Abstract
Several aspects may influence the performance achieved by a classifier created by a Machine Learning system. One of these aspects is related to the difference between the number of examples belonging to each class. When the difference is large, the learning system may have difficulties to learn the concept related to the minority class. In this work, we discuss some methods to decrease the number of examples belonging to the majority class, in order to improve the performance of the minority class. We also propose the use of the VDM metric in order to improve the performance of the classification techniques. Experimental application in a real world dataset confirms the efficiency of the proposed methods.
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Batista, G.E.A.P.A., Carvalho, A.C.P.L.F., Monard, M.C. (2000). Applying One-Sided Selection to Unbalanced Datasets. In: Cairó, O., Sucar, L.E., Cantu, F.J. (eds) MICAI 2000: Advances in Artificial Intelligence. MICAI 2000. Lecture Notes in Computer Science(), vol 1793. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720076_29
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DOI: https://doi.org/10.1007/10720076_29
Publisher Name: Springer, Berlin, Heidelberg
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