A Framework of Rough Reducts Optimization Based on PSO/ACO Hybridized Algorithms

Pratiwi, L. and Choo, Yun Huoy and Muda, A. K. (2011) A Framework of Rough Reducts Optimization Based on PSO/ACO Hybridized Algorithms. In: 3rd Conference on Data Mining and Optimization (DMO), 28-29 June 2011, Selangor, Malaysia.

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Abstract

Rough reducts has contributed significantly in numerous researches of feature selection analysis. It has been proven as a reliable reduction technique in identifying the importance of attributes set in an information system. The key factor for the success of reducts calculation in finding minimal reduct with minimal cardinality of attributes is an NP-Hard problem. This paper has proposed an improved PSO/ACO optimization framework to enhance rough reduct performance by reducing the computational complexities. The proposed framework consists of a three-stage optimization process, i.e. global optimization with PSO, local optimization with ACO and vaccination process on discernibility matrix.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: component; reduct optimization; particle swarm optimization; ant colony optimization; vaccination; rough set
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
A General Works > AS Academies and learned societies (General)
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Dr. Yun-Huoy Choo
Date Deposited: 20 Oct 2011 07:21
Last Modified: 28 May 2015 02:16
URI: http://eprints.utem.edu.my/id/eprint/147
Statistic Details: View Download Statistic

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