Combining one-class classifiers via meta learning

Authors:
Eitan Menahem;Lior Rokach;Yuval Elovici
Affiliations:
Ben-Gurion University of the Negev, Be'er Sheva, Israel;Ben-Gurion University of the Negev, Be'er Sheva, Israel;Ben-Gurion University of the Negev, Be'er Sheva, Israel
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

Selecting the best classifier among the available ones is a difficult task, especially when only instances of one class exist. In this work we examine the notion of combining one-class classifiers as an alternative for selecting the best classifier. In particular, we propose two one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles. Furthermore, we propose a new one-class ensemble scheme, TUPSO, which uses meta-learning to combine one-class classifiers. Our experiments demonstrate the superiority of TUPSO over all other tested ensembles and show that the TUPSO performance is statistically indistinguishable from that of the hypothetical best classifier.