[2P1-J-2] Machine learning: conquests of limits

[2P1-J-2-02] Imbalanced Classification with Near-misses for Binary Decision-making

〇Akira Tanimoto^1,2,3, So Yamada¹, Takashi Takenouchi^2,4, Hisashi Kashima^2,3 (1. NEC, 2. Riken, 3. Kyoto University, 4. Future University Hakodate)

Keywords:Privileged information, Cost-sensitive learning, Imbalanced classification

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.