基于邻域粗糙集的高维类不平衡数据在线流特征选择

doi:10.16451/j.cnki.issn1003-6059.201908006

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Abstract

In many real world applications, data is dynamically generated at different time periods in addition to high-dimensional imbalanced features. An high-dimensional class-imbalanced online feature selection algorithm based on neighborhood rough set is proposed. The algorithm design is based on rough dependency calculation formula of small class significance. Meanwhile, three evaluation criteria of online relevance analysis, online redundancy analysis and online significance analysis, are presented to select features with high separability between majority and minority classes. Experimental results on seven high-dimensional and class-imbalanced datasets show that the proposed method can effectively select a better feature subset with better performance.

Key words： Online Feature Selection High-Dimensional and Class-Imbalance Data Neighborhood Rough Set Rough Dependence

Received: 01 March 2019

ZTFLH:

TP 18

Fund:

Supported by National Natural Science Foundation of China(No.61672272), Natural Science Foundation of Fujian Province(No.2018J01548,2018J01547), Technology Project of Education Department of Fujian Province(No.JT180318)

Corresponding Authors: LIN Yaojin(Corresponding author), Ph.D., professor. His research interests include data mining and machine learning.

About author:: CHEN Xiangyan, master student. His research interests include data mining.WANG Chenxi, master, lecturer. Her research interests include data mining.

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Cite this article:

CHEN Xiangyan,LIN Yaojin,WANG Chenxi. Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Neighborhood Rough Set[J]. , 2019, 32(8): 726-735.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201908006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I8/726

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