A bipartite matching-based feature selection for multi-label learning
Many real-world data have multiple class labels known as multi-label data, where the labels are correlated with each other, and as such, they are not independent. Since these data are usually high-dimensional, and the current multi-label feature selection methods have not been precise enough, then a...
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Veröffentlicht in: | International journal of machine learning and cybernetics 2021-02, Vol.12 (2), p.459-475 |
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Sprache: | eng |
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