已经表明,无监督的离群检测方法可以适应一类分类问题(Janssens和Postma,在:第18届比利时-荷兰年度机器学习论文集,第56-64页,2009年;Janssens等人。在:2009年ICMLA机器学习与应用国际会议论文集,IEEE计算机学会,pp147-153,2009.10.1109/ICMLA.2009.16)。在本文中,我们专注于一类分类算法与这种适应的无监督异常检测方法的比较,在几个重要方面改进了以前的比较研究。我们在严格的实验设置中研究了许多一类分类和无监督的离群检测方法,将它们在具有不同特征的大量数据集上进行比较,使用不同的绩效指标。与以前的比较研究相比,其中模型(算法,参数)是通过使用来自两个类(离群值和离群值)的示例来选择的,在这里,我们还研究和比较了在没有离群类示例的情况下选择模型的不同方法,这对于实际应用来说更现实,因为标记的异常值很少可用。我们的研究结果表明,总的来说,SVDD和GMM表现最好,无论是否将地面实况用于参数选择。然而,在特定的应用场景中,其他方法表现出更好的性能。将一类分类器组合成集合在准确性方面比单个方法表现出更好的性能,只要合奏成员选择得当。
■在线版本包含补充材料,可在10.1007/s10618-023-00931-x获得。
It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in: Proceedings of the 18th annual Belgian-Dutch on machine learning, pp 56-64, 2009; Janssens et al. in: Proceedings of the 2009 ICMLA international conference on machine learning and applications, IEEE Computer Society, pp 147-153, 2009. 10.1109/ICMLA.2009.16). 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. In contrast to previous comparison studies, where the models (algorithms, parameters) are selected by using examples from both classes (outlier and inlier), here we also study and compare different approaches for model selection in the absence of examples from the outlier class, which is more realistic for practical applications since labeled outliers are rarely available. Our results showed that, overall, SVDD and GMM are top-performers, regardless of whether the ground truth is used for parameter selection or not. However, in specific application scenarios, other methods exhibited better performance. Combining one-class classifiers into ensembles showed better performance than individual methods in terms of accuracy, as long as the ensemble members are properly selected.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s10618-023-00931-x.