On the Evaluation of Outlier Detection and One-Class Classification Methods
Lorne Swersky, Henrique O. Marques, Jörg Sander, Ricardo J. G. B. Campello, Arthur Zimek · 2016
It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem. In this paper, we focus on the comparison of one-class classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. Our experiments led to conclusions that do not fully agree with those of previous work.