Abstract: Multi-label feature selection is one of the essential methods to overcome the curse of dimensionality.It reduces the feature dimension,improves the learning efficiency,and optimizes the classification performance.However,many existing feature selection algorithms hardly take label correlation into consideration,and the range of information entropies are biased within different data sets.To address those problems,this paper proposed a multi-label feature selection algorithm based on the improved label correlation.The algorithm firstly uses symmetrical uncertainty to norma-lize the information entropy,and takes normalized mutual information as relationship measurement to define the label importance,with which the label-related items in dependency and redundancy are weighted.In the end,the score function is put forward to evaluate the feature importance,and the best feature subset is selected with the highest score.Experiments demonstrate that after selecting out the concise and accurate feature subset,the multi-label classification is accelerated in terms of the performance and the efficiency with disperse features.

Key words: Dependency, Feature score, Label correlation, Multi-label feature selection, Redundancy