A new sequential covering strategy for inducing classification rules with ant colony algorithms

Otero, Fernando E. B.; Freitas, Alex A.; Johnson, Colin G.

Abstract

Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms.

Citation

Otero, F. E. B., Freitas, A. A., & Johnson, C. G. (2012). A new sequential covering strategy for inducing classification rules with ant colony algorithms. IEEE Transactions on Evolutionary Computation, 17(1), 64-76. https://doi.org/10.1109/TEVC.2012.2185846

• Journal Article Type: Article
• Acceptance Date: Feb 10, 2012
• Online Publication Date: Feb 10, 2012
• Publication Date: Feb 10, 2012
• Deposit Date: Nov 25, 2023
• Journal: IEEE Transactions on Evolutionary Computation
• Print ISSN: 1089-778X
• Electronic ISSN: 1941-0026
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 1
• Pages: 64-76
• DOI: https://doi.org/10.1109/TEVC.2012.2185846
• Public URL: https://nottingham-repository.worktribe.com/output/27379329
• Publisher URL: https://ieeexplore.ieee.org/document/6151113