Title: Using Bayesian posterior probability in confidence of attributes for feature selection

Authors: Inkyoo Park; Jongjin Park; Gyooseok Choi

Addresses: Department of Computer Science, Joongbu University, ChungNam, 312-702, South Korea ' Department of Internet, Chungwoon University, Incheon, 402-803, South Korea ' Department of Computer Science, Chungwoon University, Incheon, 402-803, South Korea

Abstract: Rough set theory is an efficient reduction technique to deal with vagueness and uncertainty. Many studies have been accomplished for the feature selection while they have been carried out to trade off the sophisticated process of feature selection algorithm against the robustness and accuracy of reducts. In this paper, a new Bayesian posterior probability-based QuickReduct (BPPQR) measure is introduced to determine the optimal attributes with the accurate strength of the association among the indiscernible subsets. Therefore, a new rough entropy-based QuickReduct algorithm which focuses on the reduction of redundant attributes is proposed in order to extract the optimal reduct and the core. The performance of the system is evaluated in MATLAB on several benchmark datasets with resides in UCI machine learning repository. The proposed heuristic approach can cope with the drawbacks of the conventional one, and the satisfying performances have been carried out in the process of feature selection in decision systems.

Keywords: Bayesian posterior probability; confidence; QuickReduct; entropy; rough sets; feature selection; rough set theory; redundant attributes; machine learning; uncertainty; decision making.

DOI: 10.1504/IJSN.2015.070418

International Journal of Security and Networks, 2015 Vol.10 No.2, pp.84 - 90

Received: 08 Oct 2014
Accepted: 15 Oct 2014
Published online: 05 Jul 2015 *

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