{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T21:14:11Z","timestamp":1764018851019},"reference-count":33,"publisher":"MIT Press - Journals","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1162\/evco_a_00155","type":"journal-article","created":{"date-parts":[[2015,6,12]],"date-time":"2015-06-12T17:13:11Z","timestamp":1434129191000},"page":"385-409","source":"Crossref","is-referenced-by-count":16,"title":["Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results"],"prefix":"10.1162","volume":"24","author":[{"given":"Fernando E. 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