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

作者：

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.

摘要

•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.

论文关键词：Feature selection,Multi-label learning,Information theory,Low-order information-theoretic terms,Probability distribution assumption

论文评审过程：Received 16 March 2022, Revised 19 August 2022, Accepted 10 October 2022, Available online 13 October 2022, Version of Record 18 October 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.109111