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

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm:... 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 c Ant-Miner 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 c Ant-Miner 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 c Ant-Miner producing ordered rules are also presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Evolutionary Computation MIT Press

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

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References (42)

Publisher
MIT Press
Copyright
© 2016 Massachusetts Institute of Technology
ISSN
1063-6560
eISSN
1530-9304
DOI
10.1162/EVCO_a_00155
pmid
26066807
Publisher site
See Article on Publisher Site

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 c Ant-Miner 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 c Ant-Miner 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 c Ant-Miner producing ordered rules are also presented.

Journal

Evolutionary Computation – MIT Press

Published: Sep 1, 2016

Keywords: Ant colony optimization; data mining; classification; sequential covering; unordered rules; comprehensibility

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