A Multi-label Feature Selection Algorithm Based on Fuzzy Neighborhood Information Entropy and Mutual Discriminant Index

WU Li-sheng; E Chen

Computer & Telecommunication ›› 2025, Vol. 1 ›› Issue (4) : 17-22.

WU Li-sheng, E Chen

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Abstract

Multi-label feature selection improves the performance of learning models by eliminating irrelevant features. However, most existing methods assume that the labels in the training set only contain simple logical values and that all relevant labels have the same effect on instances. In addition, in practical applications, the influence of different labels on instances may vary. Based on this, this paper proposes a feature selection method based on fuzzy neighborhood information entropy and mutual discriminant index. Firstly, the original multi-label datasets are transformed into label distribution datasets by using label enhancement technology. Then, the neighborhood information entropy is used to quantify the similarity relationship between samples in the label space. Finally, the feature space and the label space are combined by using the fuzzy neighborhood mutual discriminant index to identify the feature subset with significant discrimination ability. Experiments on six datasets comprehensively show that the classification performance of this algorithm is superior to that of other algorithms.

Key words

feature selection / fuzzy neighborhood / multi-label learning

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WU Li-sheng, E Chen. A Multi-label Feature Selection Algorithm Based on Fuzzy Neighborhood Information Entropy and Mutual Discriminant Index[J]. Computer & Telecommunication. 2025, 1(4): 17-22

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