Volume 8 Number 6 (Jun. 2013)
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JSW 2013 Vol.8(6): 1333-1338 ISSN: 1796-217X
doi: 10.4304/jsw.8.6.1333-1338

Weighted Clone Selection Algorithm based on Rough Set Theory

Jia Wu1, Zhihua Cai1, Xiaolin Chen1, Meng Li1, Bin Guo2
1School of Computer Science, China University of Geosciences, Wuhan 430074, P.R.China
2School of Computer Science, University of Sydney, NSW 2007, Australia

Abstract—Clone selection is a new artificial intelligence technology, with self-organization, self-learning, selfrecognition, self-memory capacity. In the traditional clone selection algorithm for data classification, all the attributes for classification have the same influence, which affects its classification performance to some extent, given an appropriate weight for each attribute value can modify this imbalance. Accordingly this, proposes a weighted clone selection algorithm based on rough set to improve the performance of clone selection. In weighted clone selection algorithm attribute weights obtained directly from the training data using rough set theory, the attribute weights was used to test Data classification. Then verify the validity of the method by the experiments of UCI data sets.

Index Terms—Clone selection, Attribute weight, Rough set Theory, Classification.


Cite: Jia Wu, Zhihua Cai, Xiaolin Chen, Meng Li, Bin Guo, "Weighted Clone Selection Algorithm based on Rough Set Theory," Journal of Software vol. 8, no. 6, pp. 1333-1338, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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