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Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery.

Salama, Khalid M., Abdelbar, Ashraf M., Otero, Fernando E.B., Freitas, Alex A. (2013) Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery. Applied Soft Computing, 13 (1). pp. 667-675. ISSN 1568-4946. (doi:10.1016/j.asoc.2012.07.026) (KAR id:34346)

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The cAnt-Miner algorithm is an Ant Colony Optimization (ACO) based technique for classification rule discovery in problem domains which include continuous attributes. In this paper, we propose several extensions to cAnt- Miner. The main extension is based on the use of multiple pheromone types, one for each class value to be predicted. In the proposed ?cAnt-Miner algorithm, an ant first selects a class value to be the consequent of a rule and the terms in the antecedent are selected based on the pheromone levels of the selected class value; pheromone update occurs on the corresponding pheromone type of the class value. The pre-selection of a class value also allows the use of more precise measures for the heuristic function and the dynamic discretization of continuous attributes, and further allows for the use of a rule quality measure that directly takes into account the confidence of the rule. Experimental results on 20 benchmark datasets show that our proposed extension improves classification accuracy to a statistically significant extent compared to cAnt-Miner, and has classification accuracy similar to the well-known Ripper and PART rule induction algorithms.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2012.07.026
Uncontrolled keywords: data mining, machine learning, ant colony optimization, classification, rule induction
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: 19 Jun 2013 16:13 UTC
Last Modified: 16 Nov 2021 10:11 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
Freitas, Alex A.:
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