Search-based cost-sensitive hypergraph learning for anomaly detection

Detecting anomaly plays a vital role in ensuring the reliability and safety of many applications. However, this task is challenging due to the unclear correlation underneath the testing 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 anomaly detection datasets, software defect prediction (SDP) datasets and outlier detection datasets (ODDS). Experimental results and comparisons with state-of-the-art methods have shown the superiority of our method.