Taking class importance into account

Authors:
José-Luis Polo;Fernando Berzal;Juan-Carlos Cubero
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Venue:
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
Year:
2006

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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.