Cost-sensitive Learning in Support Vector Machines
Giorgio Fumera, Fabio Roli · 2002
In this paper, a cost-sensitive learning method for support vector machine (SVM) classifiers is proposed. We focus on a particular case of cost-sensitive problems, namely, classification with reject option. Standard learning algorithms, the one for SVMs included, are not cost-sensitive. In particular, they can not handle the reject option. However, we show that, under the framework of the structural risk minimisation induction principle, on which standard SVMs are based, the rejection region should be determined during the training phase of a classifier, by the learning algorithm. We apply this approach to develop a cost-sensitive SVM classifier, by following Vapnik's maximum margin method to the derivation of standard SVMs.