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Label distribution feature selection based on label-specific features
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DOI：10.1007/s10489-024-05668-8.png)
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Label distribution learning, where deal with label ambiguity by describing the degree of relevance of each label to a specific instance. As a novel machine learning paradigm, the curse of dimensionality is one of the prominent problems. Feature selection is a vital preprocessing step to reduce the high dimensionality of data. However, most existing label distribution feature selection methods focus on selecting a feature subset that has relevant capabilities for all labels, ignoring label-specific features with the maximum discriminatory power for each label. To tackle this issue, a label distribution feature selection algorithm based on label-specific features is proposed in this paper. Initially, we introduce a feature selection optimization model for label distribution data that simultaneously considers common and label-specific features, leveraging sparse learning to further investigate the intricate relationships between features and labels. Subsequently, the label correlation coefficient is employed to enhance the collaborative learning effect of labels. Finally, the relevance between features and labels is taken into account to guide the feature selection process, which can effectively eliminate the redundant features. Comprehensive experiments demonstrate the advantage of our proposed method over other well-established feature selection algorithms for selecting label-specific features to label distribution data.
Keyword:
Feature selection
Label-specific features
Feature relevance
Mutual information
Label distribution learning
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
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Multi-label learning with Relief-based label-specific feature selection.png)
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