基于双空间模糊辨识关系的多标记特征选择

doi:10.16451/j.cnki.issn1003-6059.201908004

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Abstract

The existing multi-label feature selection algorithms based on fuzzy rough sets characterize the ability of distinguishing attributes from single sample space, while the ability of attributes distinguishing labels is ignored. Therefore, a multi-label feature selection algorithm based on fuzzy discernibility relations in double spaces is proposed. Firstly, two multi-label attribute measures based on fuzzy discernibility relations are defined from the perspective of samples and labels respectively. Then, two different measures are combined by introducing weights. Finally, a multi-label feature selection algorithm is constructed based on the combined measures by utilizing the forward greedy algorithm. Results of comparative experiments on five multi-label datasets verify the effectiveness of the proposed algorithm.

Key words： Multi-label Learning Feature Selection Fuzzy Rough Sets Fuzzy Discernibility Relation

Received: 15 March 2019

ZTFLH:

TP 18

Fund:

Supported by National Natural Science Foundation of China(No.61672331,61573231,61432011,61806116), Key R&D Program Project of Shanxi Province(No.201803D42102), Natural Science Foundation of Shanxi Province(No.201701D121055,201801D221175), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(No.201802014), Cultivate Scientific Research Excellence Programs of Higher Education Institutions in Shanxi(2019SK036), Training Program for Young Scientific Researchers of Higher Education Institutions in Shanxi

Corresponding Authors: LI Deyu(Corresponding author), Ph.D., professor. His research interests include gra-nular computing and machine learning.

About author:: YAO Erliang, master student. His research interests include rough sets and multi-label learning.LI Yanhong, Ph.D., associate professor. Her research interests include data mining and machine learning.BAI Hexiang, Ph.D., associate professor. His research interests include granular computing and spatial data mining.ZHANG Chao, Ph.D., associate profe-ssor. His research interests include intelligent optimization computing and granular computing.

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Cite this article:

YAO Erliang,LI Deyu,LI Yanhong等. Multi-label Feature Selection Based on Fuzzy Discernibility Relations in Double Spaces[J]. , 2019, 32(8): 709-717.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201908004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I8/709

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