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Kernel Neighborhood Rough Sets Model and Its Application
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-23
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zeng, Kai& Jing, Siyuan. 2018. Kernel Neighborhood Rough Sets Model and Its Application. Complexity،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1132823
Modern Language Association (MLA)
Zeng, Kai& Jing, Siyuan. Kernel Neighborhood Rough Sets Model and Its Application. Complexity No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1132823
American Medical Association (AMA)
Zeng, Kai& Jing, Siyuan. Kernel Neighborhood Rough Sets Model and Its Application. Complexity. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1132823
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132823