One-Class Classification methods via automatic counter-example generation
András Bánhalmi · 2008
Here we propose novel methods for the One-Class Classification task and examine their applicability. Essentially, these methods extend the training set – which contains only positive examples – with artificially generated counterexamples. After, a two-class classifier is used to separate them. In this paper following a description of the existing and the newly proposed methods some problematic issues are investigated theoretically and studied empirically by applying these methods to artificial datasets. Then their efficiency is compared to those of other one-class classification methods.