Abstract
In many classification problems, some classes are more important than others from the users’ perspective. In this paper, we introduce a novel approach, weighted classification, to address this issue by modeling class importance through weights in the [0,1] interval. We also propose novel metrics to evaluate the performance of classifiers in a weighted classification context. In addition, we make some modifications to the ART classification model [1] in order to deal with weighted classification.
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Polo, JL., Berzal, F., Cubero, JC. (2007). Taking Class Importance into Account. In: Szczuka, M.S., et al. Advances in Hybrid Information Technology. ICHIT 2006. Lecture Notes in Computer Science(), vol 4413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77368-9_1
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DOI: https://doi.org/10.1007/978-3-540-77368-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77367-2
Online ISBN: 978-3-540-77368-9
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