Parallel Dual-channel Multi-label Feature Selection

Abstract

In the process of multi-label learning, feature selection methods are often adopted to solve the high-dimensionality problem in feature spaces. Most existing multi-label feature selection algorithms focus on exploring the correlation between features and labels and then obtain the target feature subset by importance ranking. These algorithms commonly use serial structures to obtain important features, which induces the excessive reliance on the ranking results and causes the loss of important features. However, the correlation between label-specific feature and label-instance is ignored. Therefore, this paper proposes Parallel Dual-channel Multi-label Feature Selection algorithm (PDMFS). We first introduce the dual-channel concept and design the algorithm model as two independent modules obtaining different feature correlation sequences, so that the algorithm can avoid the over-reliance on single feature correlation. And then, the proposed algorithm uses the subspace model to select the feature subset with the maximum correlation and minimum redundancy for each sequence, thus obtaining feature subsets under respective correlations. Finally, the subsets are cross-merged to reduce the important feature loss caused by the serial structure processing single feature correlation. The experimental results on eight datasets and statistical hypothesis testing indicate that the proposed algorithm is effective.