While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and challenge, as our society becomes increasingly interconnected and digitalized. In this paper, we focus on existing anomaly detection approaches, by empirically studying the performance of 29 semi-supervised anomaly detection algorithms on 95 benchmark imbalanced databases from the KEEL repository. These include well-established and commonly used classifiers (e.g., One-Class Support Vector Machine (ocSVM) and Isolation Forest) and recent proposals (e.g., BRM and XGBOD). Findings from our in-depth empirical study show that BRM is a robust classifier, in terms of achieving better classification results than the other 28 state-of-the-art techniques on diverse anomaly detection problems. We also observe that OCKRA, Isolation Forest, and ocSVM achieve good performance overall AUC, but poor classification results on databases where the number of objects is equal or greater than 1,460, all features are nominal, or the imbalance ratio is equal or greater than 39.14.

尽管对异常检测的研究相对深入，但是随着我们的社会日益相互联系和数字化，它仍然是一个持续引起关注和挑战的话题。在本文中，我们通过对KEEL存储库中95个基准不平衡数据库上的29种半监督异常检测算法的性能进行了实证研究，重点研究了现有的异常检测方法。这些包括完善的和常用的分类器（例如，一类支持向量机（ocSVM）和隔离林）以及最近的建议（例如，BRM和XGBOD）。从我们深入的实证研究中发现，BRM是一种强大的分类器，与其他28种针对各种异常检测问题的最新技术相比，BRM具有更好的分类结果。我们还观察到OCKRA，隔离林，