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Volume 2018, Article ID 1342562, 8 pages
Research Article

Kernel Neighborhood Rough Sets Model and Its Application

1School of Data Science, Guizhou Institute of Technology, No. 1 Caiguan Road, Guiyang 550003, China
2School of Computer Science, Leshan Normal University, Binhe Road, Leshan, Sichuan 614000, China

Correspondence should be addressed to Kai Zeng; moc.anis@kniliakgnez

Received 7 March 2018; Revised 13 June 2018; Accepted 18 July 2018; Published 23 August 2018

Academic Editor: Danilo Comminiello

Copyright © 2018 Kai Zeng and Siyuan Jing. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation. The kernel rough sets model, which has fuzziness, is susceptible to noise in the decision system. The neighborhood rough sets model can handle noisy data well but cannot describe the fuzziness of the samples. In this study, we define a novel model called kernel neighborhood rough sets, which integrates the advantages of the neighborhood and kernel models. Moreover, the model is used in the problem of feature selection. The proposed method is tested on the UCI datasets. The results show that our model outperforms classic models.