Search-based cost-sensitive hypergraph learning for anomaly detection

Detecting anomaly plays a vital role in ensuring the reliability and safety of many applica-tions. However, this task is challenging due to the unclear correlation underneath the test-ing data and the class imbalanced issue between anomalous and normal data. Moreover, it is noted that the misclassification of anomalous and normal data is usually associated with different costs in real applications. To solve these issues, we propose a search-based cost -sensitive hypergraph learning method (SCSHL) for anomaly detection. More specifically, to deal with the imbalanced issue, we first construct a target-specific subset of training data according to the testing samples and conduct a cost-sensitive feature selection method to select the effective features. To explore the unclear high-order correlation underneath the data, we employ the hypergraph structure to formulate the complex relationship among the testing data and further combine the cost information into hypergraph learning and conduct label propagation on the cost-sensitive hypergraph structure. To evaluate the effectiveness of the proposed method, we have conducted experiments on industry anom-aly detection datasets, software defect prediction (SDP) datasets and outlier detection data -sets (ODDS). Experimental results and comparisons with state-of-the-art methods have shown the superiority of our method. (c) 2022 Published by Elsevier Inc.

Automated summary

In this paper, a search-based cost-sensitive hypergraph learning method is proposed for anomaly detection. The method effectively addresses the issues of class imbalance and unclear correlation commonly encountered in anomaly detection tasks. Experimental results on industry datasets and other benchmark datasets demonstrate the effectiveness and superiority of the proposed method.