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A New Version of the Ant-Miner Algorithm Discovering Unordered Rule Sets

Smaldon, James and Freitas, Alex A. (2006) A New Version of the Ant-Miner Algorithm Discovering Unordered Rule Sets. In: Keijzer, Maarten, ed. Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, New York, USA, pp. 43-50. ISBN 1-59593-186-4. (doi:10.1145/1143997.1144004) (KAR id:14462)

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The Ant-Miner algorithm, first proposed by Parpinelli and colleagues, applies an ant colony optimization heuristic to the classification task of data mining to discover an ordered list of classification rules. In this paper we present a new version of the Ant-Miner algorithm, which we call Unordered Rule Set Ant-Miner, that produces an unordered set of classification rules. The proposed version was evaluated against the original Ant-Miner algorithm in six public-domain datasets and was found to produce comparable results in terms of predictive accuracy. However, the proposed version has the advantage of discovering more modular rules, i.e., rules that can be interpreted independently from other rules - unlike the rules in an ordered list, where the interpretation of a rule requires knowledge of the previous rules in the list. Hence, the proposed version facilitates the interpretation of discovered knowledge, an important point in data mining.

Item Type: Book section
DOI/Identification number: 10.1145/1143997.1144004
Uncontrolled keywords: data mining, bioinformatics, classification, ant colony optimization
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04 UTC
Last Modified: 16 Nov 2021 09:52 UTC
Resource URI: (The current URI for this page, for reference purposes)
Freitas, Alex A.:
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