An Optimization of SMOTE for Anomaly Detection Based on High Contribution Sample Screening

Detecting anomalies with high accuracy for data with imbalanced class distribution is an important goal for many real-world applications. The imbalance between the normal and the abnormal classes is the main challenge of anomaly detection, which is traditionally addressed by the Synthetic Minority Over-sampling Techniques (SMOTE). In this paper, a new High-Contribution SMOTE is proposed to overcome the original SMOTE's limitation of not considering the high contribution from the boundary data for anomaly detection. First, the set of high-contribution samples is screened with a classifier constructed by the generated Receiver Operating Characteristic (ROC) curves, consisting of some minority class samples that are prone to misclassification. Then, these high-contribution samples are used to synthesize new minority class samples, different from the randomly selected samples in the original SMOTE. This new idea enhances the contribution of the boundary data and obtains a data distribution closer to the true picture, which is expected to significantly improve the performance of anomaly detection. Finally, comparison experiments with the Original SMOTE and the Borderline SMOTE are carried out, and the results show that the High-Contribution SMOTE proposed in this paper obtains the higher Recall Score and
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