A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers
John Hancock, Richard A. Bauder, Taghi M. Khoshgoftaar · 2023
One-class classifiers hold promise for applications like fraudulent credit card transaction identification. However, interpreting these models to understand which features drive predictions is challenging. Such an understanding is necessary to avoid brute-force approaches to feature selection. This paper explores SHAP (SHapley Additive exPlanations) for feature selection with one-class classffiers on a credit card fraud dataset. We apply SHAP to select key features and evaluate One-Class Gaussian mixture models and One-Class Support Vector Machines. Statistical analysis tests show Gaussian mixture models built with SHAP-selected features perform significantly better than Gaussian mixture models built without feature selection. To the best of our knowledge, we are the first to show the benefit of SHAP-based feature selection to One-Class Gaussian mixture models. Moreover, we show that robust performance with features of the full dataset may be a prerequisite in order for SHAP feature selection to impart further gains. Our results provide novel evidence that SHAP can identify informative features for one-class classifiers.