title: Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results.
abstract: Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.
authors.affiliation: University of Kent, Chatham Maritime, UK F.E.B.Otero@kent.ac.uk.
authors.forename: Fernando E B
authors.initials: FE
authors.lastname: Otero
authors.affiliation: University of Kent, Canterbury, UK A.A.Freitas@kent.ac.uk.
authors.forename: Alex A
authors.initials: AA
authors.lastname: Freitas
mesh_terms: D000465:Algorithms; D000818:Animals; D001000:Ants / Q000502:physiology*; D019295:Computational Biology
keywords: Ant colony optimization; classification; comprehensibility; data mining; sequential covering; unordered rules