SHAP as a Data Reduction Technique for Highly Imbalanced Big Data

Fraud detection through the classification of highly imbalanced Big Data is an exciting area of Machine Learning research. On the one hand, in certain fraud detection application domains, the use of One-Class classifiers is an overlooked opportunity. On the other hand, for researchers faced with the task of building Machine Learning models for identifying fraud, when only legitimate transaction data is available, One-Class Classifiers are indispensable. We investigate the efficacy of SHapley Additive exPlanations (SHAP) as a feature selection technique for One-Class classification tasks. In this study we utilize authentic data from the Credit Card fraud and Medicare insurance fraud application domains. Our contribution is to show that researchers can use SHAP in conjunction with One-Class Classifiers to do feature selection on highly imbalanced datasets, and then build models, with the selected features, that yield performance similar to, or better than, models built using all features. Our results in Big Medicare data fraud detection show that an over 90% data reduction through feature selection can nevertheless coincide with the best performance in terms of Area under the Precision Recall Curve.