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| Feature Selection Algorithm Based on Neighborhood Valued Tolerance Relation Rough Set Model |
| YAO Sheng1,2, XU Feng2, ZHAO Peng1,2, WANG Jie2, CHEN Ju2 |
1.Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education,Anhui University, Hefei 230039
2. College of Computer Science and Technology, Anhui University, Hefei 230601 |
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Abstract The existing methods of feature selection are mostly based on tolerance relation in the numerical incomplete information system.However, the data similarity characterization is too loose in these approaches. Therefore, the rough set model of neighborhood valued tolerance relation is proposed in this paper. The neighborhood valued tolerance condition entropy is defined on the basis of the model. And the related properties are analyzed.Finally, the corresponding algorithm is constructed according to the monotonicity of neighborhood valued tolerance condition entropy. Experimental results show that the proposed algorithm is superior to the existing algorithms in terms of the feature selection results, arithmetic operation time and classification accuracy.
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Received: 17 January 2017
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About author:: (YAO Sheng, born in 1979, Ph.D., lecturer. Her research interests include rough set theory and granular computing.)
(XU Feng(Corresponding author), born in 1993, master student. His research interests include rough set theory.)
(ZHAO Peng, born in 1976, Ph.D., associate professor. Her research interests include image processing and data mining.)
(WANG Jie, born in 1993, master student. His research interests include rough set theory.)
(CHEN Ju, born in 1993, master student. Her research interests include rough set theory.) |
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2017, Vol. 30 
