Combining one-class classifiers via meta learning Conference Paper uri icon


  • 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 …

publication date

  • October 27, 2013