Attribute Reduction Based on Consistent Covering Rough Set and Its Application
Joint Authors
Xia, Kewen
Bai, Jianchuan
Lin, Yongliang
Wu, Panpan
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-02
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
As an important processing step for rough set theory, attribute reduction aims at eliminating data redundancy and drawing useful information.
Covering rough set, as a generalization of classical rough set theory, has attracted wide attention on both theory and application.
By using the covering rough set, the process of continuous attribute discretization can be avoided.
Firstly, this paper focuses on consistent covering rough set and reviews some basic concepts in consistent covering rough set theory.
Then, we establish the model of attribute reduction and elaborate the steps of attribute reduction based on consistent covering rough set.
Finally, we apply the studied method to actual lagging data.
It can be proved that our method is feasible and the reduction results are recognized by Least Squares Support Vector Machine (LS-SVM) and Relevance Vector Machine (RVM).
Furthermore, the recognition results are consistent with the actual test results of a gas well, which verifies the effectiveness and efficiency of the presented method.
American Psychological Association (APA)
Bai, Jianchuan& Xia, Kewen& Lin, Yongliang& Wu, Panpan. 2017. Attribute Reduction Based on Consistent Covering Rough Set and Its Application. Complexity،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1143613
Modern Language Association (MLA)
Bai, Jianchuan…[et al.]. Attribute Reduction Based on Consistent Covering Rough Set and Its Application. Complexity No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1143613
American Medical Association (AMA)
Bai, Jianchuan& Xia, Kewen& Lin, Yongliang& Wu, Panpan. Attribute Reduction Based on Consistent Covering Rough Set and Its Application. Complexity. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1143613
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143613