A problem-agnostic approach to feature selection and analysis using SHAP - John T. Hancock; Taghi M. Khoshgoftaar; Qianxin Liang (Journal of Big Data)
Abstract Feature selection is an effective data reduction technique. SHapley Additive exPlanations (SHAP) can be used to provide a feature importance ranking for models built with labeled or unlabeled data. Thus, one may use the SHAP feature importance ranking in a feature selection technique by selecting the k highest ranking features. Furthermore, this SHAP-based feature selection technique is applicable regardless of the availability of labels for data. We use the Kaggle Credit Card Fraud detection dataset to simulate three label availability scenarios. When no labeled data is available, unsupervised learners should be used. We explore feature selection for data reduction with Isolation Forest and SHAP for this case. When data of one class is available, a one-class classifier, such as Gaussian Mixture Model (GMM) can be used in combination with SHAP for determining feature importance, and for feature selection. Finally, if labeled data from both classes is available a binary-class classifier can be used in conjunction with SHAP for data reduction. Our contribution is to provide a comparative analysis of features selected in the three label availability scenarios. Our primary conclusion is that feature sets may be reduced with SHAP without compromising performance. To the best of our knowledge, this is the first study to explore a feature analysis technique, applicable in the three label availability scenarios.