Creating Universum for class imbalance via locality and its application in multiview subspace learning

Abstract

Since the existing methods for generating Universum fail in class imbalance cases, we propose a new method for creating Universum via locality and betweenness, named L-CIBU. Compared to existing methods for generating Universum, L-CIBU not only generates appropriate Universum in the case of class imbalance but also has higher efficiency. Given the existence of class imbalance in multiview learning, we introduce Universum to multiview subspace learning (MvSL) to further enhance its performance and L-CIBU happens to be able to overcome this problem. As far as we know, this is the first time to introduce Universum into MvSL with more than two views. Specifically, we introduce Universum created by L-CIBU into multiview discriminant analysis (MvDA) and propose Universum MvDA (UMvDA). By analyzing several randomly generated imbalanced datasets, it is intuitively demonstrated that L-CIBU is more effective than FIBU and CIBU in the case of class imbalance. The experimental results on two real datasets show that the time required for creating the Universum using L-CIBU is 25%∼95% less than that of FIBU and CIBU. Moreover, UMvDA achieves an accuracy improvement of approximately 1%∼6% compared to MvDA, Universum canonical correlation analysis (UCCA), and Universum discriminant canonical correlation analysis (UDCCA).