Skip to main content

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

Otero, Fernando E.B., Freitas, Alex A., Johnson, Colin G. (2013) A new sequential covering strategy for inducing classification rules with ant colony algorithms. IEEE Transactions on Evolutionary Computation, 17 (1). pp. 64-76. ISSN 1089-778X. (doi:10.1109/TEVC.2012.2185846) (KAR id:34305)

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.

Item Type: Article
DOI/Identification number: 10.1109/TEVC.2012.2185846
Uncontrolled keywords: rule induction, ant colony optimization, data mining, machine learning, classification
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 17 Jun 2013 17:06 UTC
Last Modified: 16 Nov 2021 10:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34305 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.