Fuzzy information gain ratio-based multi-label feature selection with label correlation

Ying Yu, Meiyue Lv, Jin Qian, Jingqin Lv,Duoqian Miao

International Journal of Machine Learning and Cybernetics(2024)

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摘要
Multi-label feature selection aims to mitigate the curse of dimensionality in multi-label data by selecting a smaller subset of features from the original set for classification. Existing multi-label feature selection algorithms frequently neglect the inherent uncertainty in multi-label data and fail to adequately consider the relationships between features and labels when assessing the importance of features. In response to this challenge, a Fuzzy Information Gain Ratio-based multi-label feature selection considering Label Correlation (FIGR_LC) algorithm is proposed. FIGR_LC evaluates feature importance by combining the relationship between features and individual labels, as well as the correlation between features and label sets. Subsequently, a feature ranking is established based on these feature weights. Experimental results substantiate the effectiveness of FIGR_LC, showcasing its superiority over several established feature selection methods.
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关键词
Multi-label feature selection,Fuzzy information gain ratio,Fuzzy rough sets
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