Abstract Attribute reduction is an important process in rough set theory. Minimal attribute reductions are expected to help clients make better decisions in some cases. In this paper, a heuristic approach for solving the minimal attribute reduction problem (MARP) is proposed based on the ant colony optimization (ACO) metaheuristic. Firstly, the MARP is trasformed into an assignment which minimizes the cost in a constraint satisfaction model. Then, a preprocessing step is introduced that removes the redundant data in a discernibility matrix through the absorbtion operator to favor a smaller exploration of the search space at a lower cost. Next, an algorithm, RACO, is developed to solve the MARP. Finally, the simulation results show that the proposed approach finds more minimal attribute reductions efficiently in most cases.
2011, Vol. 24 
