Multi-label learning based on instance correlation and feature redundancy

Abstract

Multi-label classification is a machine-learning task that simultaneously processes instances associated with multiple labels. Label-specific feature learning selects each label's most discriminative feature subset, effectively reducing the feature dimension and improving the classification performance. However, most methods only consider label correlation, ignoring the correlation between instances and feature redundancy. To solve this problem, a multi-label classification method based on instance correlation and feature redundancy is proposed. The proposed method merges instance correlation by updating the data set and removes redundant features by calculating mutual information. By jointly considering label correlation, instance correlation, and feature redundancy, our method promotes effective multi-label feature selection. The experimental results on ten data sets demonstrate the effectiveness of the proposed method.