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Multi-label learning based on instance correlation and feature redundancy
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DOI：10.1016/j.patrec.2023.10.029.png)
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Abstract
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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.
Keywords:
Multi-label classification
Label-specific features
Instance correlation
Feature selection
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Learning multi-label label-specific features via global and local label correlations.png)
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