A New Sequential Covering Strategy for Inducing Classification Rules With Ant Colony Algorithms

• Published in: IEEE transactions on evolutionary computation Vol. 17; no. 1; pp. 64 - 76
• Main Authors: Otero, F. E. B., Freitas, A. A., Johnson, C. G.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-02-2013; Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Algorithmics. Computability. Computer arithmetics; Ant colony optimization; Applied sciences; classification; Classification algorithms; Computational modeling; Computer science; control theory; systems; data mining; Data processing. List processing. Character string processing; Exact sciences and technology; Memory organisation. Data processing; Prediction algorithms; Predictive models; rule induction; sequential covering; Software; Theoretical computing; Training; Ant colony optimization; Artificial life; Data analysis; Statistical analysis; Sequential method; Classification; Swarm intelligence; Data mining; sequential covering; Inference rule; rule induction
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2012.2185846

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.