The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Future Generation Computer Systems
Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Ant colony optimization theory: a survey
Theoretical Computer Science
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Order based genetic algorithms for the search of approximate entropy reducts
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Evolutionary Rough Feature Selection in Gene Expression Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Exploring ant-based algorithms for gene expression data analysis
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Involving New Local Search in Hybrid Genetic Algorithm for Feature Selection
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
An Efficient Feature Selection Using Ant Colony Optimization Algorithm
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Soft fuzzy rough sets for robust feature evaluation and selection
Information Sciences: an International Journal
Transactions on rough sets XII
The Knowledge Engineering Review
A new fitness function for solving minimum attribute reduction problem
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Solving the attribute reduction problem with ant colony optimization
Transactions on rough sets XIII
A novel ensemble algorithm for biomedical classification based on Ant Colony Optimization
Applied Soft Computing
A new hybrid ant colony optimization algorithm for feature selection
Expert Systems with Applications: An International Journal
Minimum cost attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
A novel and better fitness evaluation for rough set based minimum attribute reduction problem
Information Sciences: an International Journal
Investigating memetic algorithm in solving rough set attribute reduction
International Journal of Computer Applications in Technology
On an optimization representation of decision-theoretic rough set model
International Journal of Approximate Reasoning
An Exponential Monte-Carlo algorithm for feature selection problems
Computers and Industrial Engineering
Immune ant swarm optimization for optimum rough reducts generation
International Journal of Hybrid Intelligent Systems
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Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we introduce a new approach based on ant colony optimization (ACO) for attribute reduction. To verify the proposed algorithm, numerical experiments are carried out on thirteen small or medium-sized datasets and three gene expression datasets. The results demonstrate that this algorithm can provide competitive solutions efficiently.