A unified low-order information-theoretic feature selection framework for multi-label learning

Wanfu Gao, Pingting Hao, Yang Wu, Ping Zhang

Abstract: Highlights•Clearing up two basic types of probability distribution assumption.•Concluding one unified framework regarding multi-label approaches.•Proposing a multi-label feature selection approach based on the unified framework.•Numerous experiments are conducted to demonstrate the superiority of our method.