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Improving the interpretability of classification rules discovered by an ant colony algorithm.

Otero, Fernando E.B., Freitas, Alex A. (2013) Improving the interpretability of classification rules discovered by an ant colony algorithm. In: Improving the interpretability of classification rules discovered by an ant colony algorithm. . pp. 73-80. ACM Press., New York, NY, USA. ISBN 978-1-4503-1963-8.

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Abstract

The vast majority of Ant Colony Optimization (ACO) al- gorithms 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 motivation for discovering a set of rules is to improve the interpretation of individual rules and 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 and the cAnt-MinerPB producing ordered rules are also presented.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: ant colony optimization, classification, data mining, machine learning
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Fernando Otero
Date Deposited: 22 Jul 2013 12:44 UTC
Last Modified: 01 Aug 2019 10:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34827 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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