An improved approach to attribute reduction with ant colony optimization |
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Authors: | Ting-quan Deng Ming-hua Ma Xin-xia Wang Yue-tong Zhang |
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Institution: | 1.College of Science,Harbin Engineering University,Harbin, Heilongjiang,P.R.China;2.College of Science,Heilongjiang Institute of Science and Technology,Harbin, Heilongjiang,P.R.China;3.Huawei Nanjing Research Institution,Huawei Technologies Co. Ltd,Nanjing, Jiangsu,P.R.China |
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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. |
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