Kernel Neighborhood Rough Sets Model and Its Application. (23rd August 2018)

• Record Type: Journal Article
• Authors: Zeng, Kai; Jing, Siyuan
• Other Names: Comminiello Danilo Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-08-23
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/1342562
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.