Solving the Attribute Reduction Problem with Ant Colony Optimization

  • Authors:

  • Hong Yu;Guoyin Wang;Fakuan Lan

  • Affiliations:

  • Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China 400065 and Department of Computer Science, University of Regina, Regina, C ...;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China 400065;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China 400065

  • Venue:
  • RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
  • Year:
  • 2008

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Abstract

Attribute reduction is an important process in rough set theory. More minimal attribute reductions are expected to help clients make decisions in some cases, though the minimal attribute reduction problem (MARP) is proved to be a NP-hard problem. In this paper, we propose a new heuristic approach for solving the MARP based on the ant colony optimization (ACO) metaheuristic. We first model the MARP as finding an assignment which minimizes the cost in a graph. Afterward, we introduce a preprocessing step that removes the redundant data in a discernibility matrix through the absorbtion operator, the goal of which is to favor a smaller exploration of the search space at a lower cost. We then develop a new algorithm R-ACO for solving the MARP. Finally, the simulation results show that our approach can find more minimal attribute reductions more efficiently in most cases.