Data Reduction to Improve the Performance of One-Class Classifiers on Highly Imbalanced Big Data

We propose and demonstrate a data reduction technique to improve the performance of one-class classifiers for highly imbalanced Big Data. We focus on insurance fraud detection in Medicare Part D prescription drug claims data. An initial comparison of the performance One-Class Support Vector Machines (SVMs) and One-Class Gaussian Mixture Models (GMMs) showed One-Class GMMs significantly out-performing One-Class SVMs. Therefore, we utilize One-Class Gaussian Mixture Models (GMMs). To enable faster training, we experiment with different levels of reduction in the size of the training data. Our novel application of a data reduction technique to one-class classification maintains model performance in terms of Area Under the Receiver Operating Characteristic Curve (AUC) and Area Under the Precision Recall Curve (AUPRC) scores, while simultaneously lowering model training time and computational resource consumption. Statistical tests confirm models trained on 20 percent of the training data perform equally well in terms of AUC and AUPRC. Furthermore, models trained on five percent of the training data yield AUC scores equivalent to models trained with all available training data. To the best of our knowledge, we are the first to demonstrate a viable data reduction technique for one-class classifiers applied to severely imbalanced Big Data.