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Using binary classifiers for one-class classification
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DOI：10.1016/j.eswa.2021.115920.png)
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摘要
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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.
Keyword:
One-class classification
One-class classifier
Binary classifier
Ensemble learning
One-against-rest
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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