  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 new 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.
