Imbalanced Classification with Near-misses for Binary Decision-making

Abstract

We consider a prediction-based decision-making problem, in which a binary decision corresponds to whether or not a numerical variable is predicted to exceed a given threshold. The final goal is to predict a binary label, however, we can exploit the numerical variable in the training phase as side-information. In addition, we focus on class-imbalanced situation. We investigate on an idea of using near-miss samples, which is specified by the numerical variable, to deal with the class-imbalance. We present the benefit of exploiting the side-information theoretically as well as experimentally.