Distance-based Probabilistic Data Augmentation for Synthetic Minority Oversampling

Abstract

Class imbalance can adversely affect the performance of machine learning for prediction and classification. One approach to address the class imbalance problem is synthetic minority oversampling. Oversampling approaches can be broadly categorized as either being structural or statistical in nature. Structural approaches generally have the advantage of identifying and oversampling those minority data points that best facilitate class separation, while statistical approaches model the underlying distribution from which the minority samples can be drawn. In this article, we formulate a distance-based approach that generates samples by both modeling the underlying minority class distribution and by geometrically considering those borderline samples entangled in the majority class. We demonstrate the efficacy of our approach operating on the Class-Imbalance data set from UCI by comparing its mean accuracy, AUC and F 1 -score performance against both statistical and structural synthetic minority oversampling methods.