Rough set algorithms in classification problem
Rough set methods and applications
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
A reduction algorithm meeting users' requirements
Journal of Computer Science and Technology
Ant Colony Optimization
Ant colony optimization theory: a survey
Theoretical Computer Science
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
An efficient ant colony optimization approach to attribute reduction in rough set theory
Pattern Recognition Letters
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
Knowledge reduction in random information systems via Dempster-Shafer theory of evidence
Information Sciences: an International Journal
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
A qos-aware web services selection model using AND/OR graph
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
A novel and better fitness evaluation for rough set based minimum attribute reduction problem
Information Sciences: an International Journal
Core set analysis in inconsistent decision tables
Information Sciences: an International Journal
Feature selection with test cost constraint
International Journal of Approximate Reasoning
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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 an 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 absorption operator and the cutting 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.