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Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

Otero, Fernando E.B., Freitas, Alex A. (2016) Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results. Evolutionary Computation, 24 (3). pp. 385-409. ISSN 1063-6560. (doi:10.1162/EVCO_a_00155) (KAR id:49076)

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The vast majority of Ant Colony Optimization (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB 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-MinerPB 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-MinerPB producing ordered rules are also presented.

Item Type: Article
DOI/Identification number: 10.1162/EVCO_a_00155
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: 18 Jun 2015 10:32 UTC
Last Modified: 16 Feb 2021 13:25 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
Freitas, Alex A.:
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