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Multi-label-Specific Features Learning Algorithm Based on Label Importance and Fuzzy Rough Set
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DOI：10.1007/s40815-024-01776-2.png)
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Label-specific features learning is a prominent research hotspot in the field of multi-label learning, which aims to construct a classification model based on the distinctive features of each label rather than the whole features. Existing approaches regarding label-specific features usually assume that the importance of each label to an instance is equal. However, this popular strategy might be suboptimal as the importance of labels actually is different. In this paper, a multi-label-specific features learning algorithm based on label importance and fuzzy rough set is proposed. First, the importance of labels is measured based on the similarity of instances, which not only preserves the ranking of relevant and irrelevant labels, but also follows the principles of smoothness and normalization. Second, the correlation between labels is analyzed, and label-specific features of each label are extracted through a fuzzy rough set model. Experiments on several public available data sets demonstrate the effectiveness of the proposed algorithm.
Keyword:
Multi-label learning
Label-specific features
Label importance
Label neighborhood
Fuzzy rough set
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3.6
论文数:
2.2K
被引数:
4.3K
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Learning multi-label label-specific features via global and local label correlations通过全局和局部标签相关性学习多标签标签特定特征.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
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分享收藏An extensive experimental comparison of methods for multi-label learningPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
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