Counter-Example Generation-Based One-Class Classification

Authors:
András Bánhalmi;András Kocsor;Róbert Busa-Fekete
Affiliations:
Research Group on Artificial Intelligence of the Hungarian Academy of Sciences, and of the University of Szeged, H-6720 Szeged, Aradi vértanúk tere 1., Hungary;Research Group on Artificial Intelligence of the Hungarian Academy of Sciences, and of the University of Szeged, H-6720 Szeged, Aradi vértanúk tere 1., Hungary;Research Group on Artificial Intelligence of the Hungarian Academy of Sciences, and of the University of Szeged, H-6720 Szeged, Aradi vértanúk tere 1., Hungary
Venue:
ECML '07 Proceedings of the 18th European conference on Machine Learning
Year:
2007

Citing 6
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Abstract

For One-Class Classification problems several methods have been proposed in the literature. These methods all have the common feature that the decision boundary is learnt by just using a set of the positive examples. Here we propose a method that extends the training set with a counter-example set, which is generated directly using the set of positive examples. Using the extended training set, a binary classifier (here 茂戮驴-SVM) is applied to separate the positive and the negative points. The results of this novel technique are compared with those of One-Class SVM and the Gaussian Mixture Model on several One-Class Classification tasks.