Using binary classifiers for one-class classification

In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets.

Automated summary

This paper proposes a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification, which allows the use of any supervised classification algorithms and extensive comparison of various learning algorithms to obtain a more competent one-class classifier. Experimental validation using benchmark datasets demonstrates the effectiveness of BCE-OC.