Universum driven cost-sensitive learning method with asymmetric loss function

Abstract

Class imbalance learning (CIL) has been a critical yet challenging topic for a long time. To address the issue, this paper proposes a cost-sensitive learning method based on the linear-exponential (LINEX) loss function and the Universum (UCSSVM). On one hand, the asymmetry of LINEX loss function is leveraged to realize cost-sensitive learning, by treating each instance discriminately. On the other hand, to enhance the classification performance, a novel and effective strategy is put forward to generate Universum with the assistance of hard-to-classify samples (small margin distance). The mini-batch gradient descent (MBGD) approach is adopted to optimize UCSSVM. Meanwhile, extensive experiments demonstrate its superiority in CIL and the necessity of using Universum.