A Novel Classification Approach through Integration of Rough Sets and Back-Propagation Neural Network
Joint Authors
Si, Lei
Tan, Chao
Wang, Zhong-bin
Liu, Xin-hua
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-24
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Classification is an important theme in data mining.
Rough sets and neural networks are the most common techniques applied in data mining problems.
In order to extract useful knowledge and classify ambiguous patterns effectively, this paper presented a hybrid algorithm based on the integration of rough sets and BP neural network to construct a novel classification system.
The attribution values were discretized through PSO algorithm firstly to establish a decision table.
The attribution reduction algorithm and rules extraction method based on rough sets were proposed, and the flowchart of proposed approach was designed.
Finally, a prototype system was developed and some simulation examples were carried out.
Simulation results indicated that the proposed approach was feasible and accurate and was outperforming others.
American Psychological Association (APA)
Si, Lei& Liu, Xin-hua& Tan, Chao& Wang, Zhong-bin. 2014. A Novel Classification Approach through Integration of Rough Sets and Back-Propagation Neural Network. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-498940
Modern Language Association (MLA)
Si, Lei…[et al.]. A Novel Classification Approach through Integration of Rough Sets and Back-Propagation Neural Network. Journal of Applied Mathematics No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-498940
American Medical Association (AMA)
Si, Lei& Liu, Xin-hua& Tan, Chao& Wang, Zhong-bin. A Novel Classification Approach through Integration of Rough Sets and Back-Propagation Neural Network. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-498940
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-498940