信息熵最小约简问题的若干随机优化算法

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Abstract Existing heuristic attribute reduction algorithms generally fail to get a minimum entropy-based attribute reduction of a decision table. Some stochastic optimization algorithms are discussed to solve the problem of entropy-based attribute reduction. Firstly, a proper fitness function is defined to transform the minimum attribute reduction problem into a fitness optimization problem without additional constraints and the equivalence of transformation is proved. Then, the solving efficiency and the solution quality of some stochastic optimization algorithms are studied such as Genetic Algorithm, Particle Swarm Optimization, Tabu search and Ant Colony Optimization. Some UCI datasets are applied to test those performances. The experimental results show that the fully informed PSO based attribute reduction algorithm with refine scheme has a higher probability to find a minimum entropy-based attribute reduction and good performance.

Key words： Stochastic Optimization Algorithms Rough Set Information Entropy Minimum Attribute Reduction Fully Informed PSO

Received: 05 January 2011

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	MA Sheng-Lan
	YE Dong-Yi

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MA Sheng-Lan,YE Dong-Yi. Research on Computing Minimum Entropy Based Attribute Reduction via Stochastic Optimization Algorithms[J]. , 2012, 25(1): 96-104.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2012/V25/I1/96

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