基于信息粒化的多标记特征选择算法

doi:10.16451/j.cnki.issn1003-6059.201802003

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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 words： Feature 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

Cite this article:

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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