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  2019, Vol. 32 Issue (8): 726-735    DOI: 10.16451/j.cnki.issn1003-6059.201908006
Granular Computing Theory and Application Research Current Issue| Next Issue| Archive| Adv Search |
Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Neighborhood Rough Set
CHEN Xiangyan1,2, LIN Yaojin1,2, WANG Chenxi1,2
1.School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000
2.Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou 363000

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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 wordsOnline 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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CHEN Xiangyan
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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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