Unsupervised Outlier Detection: A Meta-Learning Algorithm Based on Feature Selection

Abstract

Outlier detection refers to the problem of the identification and, where appropriate, the elimination of anomalous observations from data. Such anomalous observations can emerge due to a variety of reasons, including human or mechanical errors, fraudulent behaviour as well as environmental or systematic changes, occurring either naturally or purposefully. The accurate and timely detection of deviant observations allows for the early identification of potentially extensive problems, such as fraud or system failures, before they escalate. Several unsupervised outlier detection methods have been developed; however, there is no single best algorithm or family of algorithms, as typically each relies on a measure of `outlierness’ such as density or distance, ignoring other measures. To add to that, in an unsupervised setting, the absence of ground-truth labels makes finding a single best algorithm an impossible feat even for a single given dataset. In this study, a new meta-learning algorithm for unsupervised outlier detection is introduced in order to mitigate this problem. The proposed algorithm, in a fully unsupervised manner, attempts not only to combine the best of many worlds from the existing techniques through ensemble voting but also mitigate any undesired shortcomings by employing an unsupervised feature selection strategy in order to identify the most informative algorithms for a given dataset. The proposed methodology was evaluated extensively through experimentation, where it was benchmarked and compared against a wide range of commonly-used techniques for outlier detection. Results obtained using a variety of widely accepted datasets demonstrated its usefulness and its state-of-the-art results as it topped the Friedman ranking test for both the area under receiver operating characteristic (ROC) curve and precision metrics when averaged over five independent trials.

Constructing a Meta-Learner for Unsupervised Anomaly Detection

“…Few studies have proposed an automated algorithm selection in AD [17], [18], [19], [20], [21]. Of these, only one model is designed for unsupervised scenarios and utilises the meta-learner framework as presented by Rice [18].…”

“…A different approach of addressing the ASP in AD to that mentioned above has recently been presented in [20], [21]. These studies consider the unsupervised nature of AD problems but do not use offline meta-training.…”

Task-Oriented Network Abnormal Behavior Detection Method

“…ese technologies can be discriminated into shallow ML and Deep Learning (DL) according to the involved network architecture. ML-based anomaly detection approaches can be further classified as supervised [6], semisupervised [7,8], and unsupervised [9,10]. Supervised classification approaches have a high requirement for the training data, which should include as many as anomalous examples along with corresponding labels.…”