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. (KAR id:34827)
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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) |
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Uncontrolled keywords: | ant colony optimization, classification, data mining, machine learning |
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: | 22 Jul 2013 12:44 UTC |
Last Modified: | 16 Nov 2021 10:12 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/34827 (The current URI for this page, for reference purposes) |
Otero, Fernando E.B.: | ![]() |
Freitas, Alex A.: | ![]() |
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