Bicriteria Oversampling for Imbalanced Data Classification

Abstract

The paper proposes bicriteria oversampling strategy for mining imbalanced data. We use two specialized criteria for oversampling -classification potential and distance from the borderline between minority and majority instances. The potential is to be maximized and the distance minimized. The required number of synthetic examples is selected from the non-dominated set of examples produced by the evolutionary algorithm. At the final step the balanced set of examples is used by GEP classifier. Computational experiment confirmed that the approach assures high quality performance.