An improved approach to attribute reduction with ant colony optimization

Ting-quan Deng (), Ming-hua Ma, Xin-xia Wang and Yue-tong Zhang
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Ting-quan Deng: Harbin Engineering University
Ming-hua Ma: Harbin Engineering University
Xin-xia Wang: Heilongjiang Institute of Science and Technology
Yue-tong Zhang: Huawei Technologies Co. Ltd

Fuzzy Information and Engineering, 2010, vol. 2, issue 2, 145-155

Abstract: Abstract Attribute reduction problem (ARP) in rough set theory (RST) is an NPhard one, which is difficult to be solved via traditionally analytical methods. In this paper, we propose an improved approach to ARP based on ant colony optimization (ACO) algorithm, named the improved ant colony optimization (IACO). In IACO, a new state transition probability formula and a new pheromone traps updating formula are developed in view of the differences between a traveling salesman problem and ARP. The experimental results demonstrate that IACO outperforms classical ACO as well as particle swarm optimization used for attribute reduction.

Keywords: Ant colony optimization; Attribute reduction; Rough set theory (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s12543-010-0042-9

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