Multi-Label Feature Selection Based on Fuzzy Dependent Decision Entropy
Feature dimension disaster is one of the important challenges of multi-label learning.In order to seek an efficient multi-label feature selection method,this paper studies multi-label feature selection from the per-spective of fuzzy rough set and decision dependent entropy,and proposes a new method for multi-label feature selection.Firstly,a multi-label fuzzy information system is defined,and fuzzy decision entropy and fuzzy depend-ent decision entropy are proposed by using the approximate set of fuzzy label particles,and their properties are studied.A simplified definition of fuzzy dependent decision entropy based on fuzzy dependent decision entropy is proposed on multi-label fuzzy information system,and then the importance measure of features is given,and a multi-label feature selection method and algorithm based on fuzzy dependent decision entropy are given.Final-ly,the parameters and performance comparison of five indicators are carried out on 10 public multi-label data-sets.The results show that the proposed algorithm has certain effectiveness,and outperforms PMU,MDDM,and other multi-label feature selection algorithms on most indicators.
NETL
NSTL
万方数据
维普
