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| Multi-label Feature Selection Based on Fuzzy Neighborhood Similarity Relations in Double Spaces |
| XU Jiucheng1,2, SHEN Kaili1,2 |
1. College of Computer and Information Engineering, Henan Nor-mal University, Xinxiang, 453007;
2. Engineering Lab of Intelligence Business and Internet of Things of Henan Province, Henan Normal University, Xinxiang, 453007 |
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Abstract In most of the current rough set based multi-label feature selection algorithms, sample fuzziness and neighborhood relationship are ignored, the neighborhood radius needs setting manually, and attribute importance is measured in a single space. To overcome the defects of classical rough set algorithms, an algorithm of multi-label feature selection based on fuzzy neighborhood similarity in double spaces is proposed from the perspectives of feature space and label space. Firstly, an adaptive neighborhood radius calculation method is proposed and fuzzy neighborhood similarity matrix of samples in feature space is constructed. Secondly, similarities of sample in feature space and label space are obtained according to fuzzy neighborhood similarity relations. Then, the sample similarities in feature space and label space are fused by introducing weights and the attribute importance is calculated based on the fused measures. Finally, a multi-label feature selection algorithm is constructed via the forward greedy algorithm. The effectiveness of the proposed algorithm is confirmed on twelve multi-label datasets.
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Received: 18 July 2022
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| Fund:Supported by National Natural Science Foundation of China(No.61976082,62076089,62002103) |
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Corresponding Authors:
XU Jiucheng, Ph.D., professor. His research interests include data mining, granular computing and bioinformatics.
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| About author:: SHEN Kaili, master student. Her research interests include data mining and bioinforma-tics. |
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2022, Vol. 35 
