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  2018, Vol. 31 Issue (2): 123-131    DOI: 10.16451/j.cnki.issn1003-6059.201802003
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Multi-label Feature Selection Based on Information Granulation
WANG Chenxi1,2, LIN Yaojin1,2, TANG Li1, FU Wei1, LIN Peirong1
1.School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000
2.Key Laboratory of Data Science and Intelligence Application, Zhangzhou 363000

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Abstract  

Feature selection is to select a subset of features from the original feature space to yield similar or better learning performance compared with the original feature set in the task of classification. In this paper, an information granulation based multi-label feature selection is firstly proposed. Then, the label weight and sample average margin is fused. Finally, the improved neighborhood information entropy is applied to multi-label feature selection. Experiments are conducted on six datasets and five evaluation metrics, and experimental results show that the proposed algorithm is effective.

Key wordsFeature Selection      Information Granulation      Label Weighting      Multi-label Learning     
Received: 14 August 2017     
ZTFLH: TP 18  
Fund:

Supported by National Natural Science Foundation of China(No.61672272,61303131), Program for New Century Excellent Talents in University of Fujian Province

About author:: WANG Chenxi, master, lecturer. Her research interests include data mining.LIN Yaojin(Corresponding author), Ph.D., associate professor. His research interests include data mining and granular computing.TANG Li, master student. Her research interests include data mining.FU Wei, bachelor, associate professor. His research interests include data mining.LIN Peirong, bachelor, professor. His research interests include data mining.
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WANG Chenxi
LIN Yaojin
TANG Li
FU Wei
LIN Peirong
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WANG Chenxi,LIN Yaojin,TANG Li等. Multi-label Feature Selection Based on Information Granulation[J]. , 2018, 31(2): 123-131.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201802003      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I2/123
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