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An improved approach to attribute reduction with ant colony optimization

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ting-quan Deng & Ming-hua Ma & Xin-xia Wang & Yue-tong Zhang, 2010. "An improved approach to attribute reduction with ant colony optimization," Fuzzy Information and Engineering, Springer, vol. 2(2), pages 145-155, June.
    • Handle: RePEc:spr:fuzinf:v:2:y:2010:i:2:d:10.1007_s12543-010-0042-9
    DOI: 10.1007/s12543-010-0042-9
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