Abstract
Recent years, multi-label feature selection has gradually attracted significant attentions from machine learning, statistical computing and related communities and has been widely applied to diverse problems from music recognition to text mining, image annotation, etc. However, traditional multi-label feature selection methods employ cumulative summation strategy to design methods, which suffers from the problem of overestimation the redundancy of some candidate features. Additionally, the cumulative summation strategy will lead to the high value of the goal function when one candidate feature is completed related with one or few already-selected features, but it is almost independent from the majority of already-selected features. To address these issues, we propose a new multi-label feature selection method named Feature Redundancy Maximization (FRM), which combines the cumulative summation of conditional mutual information with the `maximum of the minimum' criterion. Additionally, FRM can be rewritten as another form of multi-label feature selection method that employs interaction information as a measure of feature redundancy that obtains an accurate score of feature redundancy as the number of already-selected features increases. Finally, extensive experiments are implemented on fourteen benchmark multi-label data sets in comparison to six state-of-the-art methods. The experimental results demonstrate the superiority of the proposed method.
Highlights
Recent years have witnessed an increasing number of applications involving multi-label data sets in which each instance is associated with multiple labels simultaneously
As in traditional single-label feature selection methods, multi-label feature selection methods are divided into three models: (1) filter models where the selection is independent of any classifier, (2) wrapper models where the prediction method is used as a black box to assess the importance of candidate feature subsets, and (3) embedded models where the procedure of feature selection is embedded in their learning process [1]–[4]
Afterwards, we notice that these methods adopt the cumulative summation strategy which leads to the high value of the goal function when one candidate feature is completed related with one or few already-selected features, but it is almost independent from the majority of already-selected features
Summary
Recent years have witnessed an increasing number of applications involving multi-label data sets in which each instance is associated with multiple labels simultaneously. The high-dimensionality of data is a stumbling block for multi-label learning. Multi-label feature selection is a key technique in many applications since it can speed up the learning process, improve the classification accuracy, and alleviate the effect of the curse of dimensionality. Developments on multi-label feature selection methods have been made. We focus on filter-based multi-label feature selection methods because they have higher computational efficiency.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
