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Ant colony optimization algorithm for rule based classification: Issues and potential

Al-Behadili, Hayder Naser Khraibet and Ku-Mahamud, Ku Ruhana and Sagban, Rafid (2018) Ant colony optimization algorithm for rule based classification: Issues and potential. Journal of Theoretical and Applied Information Technology, 96 (21). 7139 -7150. ISSN 1992-8645

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Classification rule discovery using ant colony optimization (ACO) imitates the foraging behavior of real ant colonies. It is considered as one of the successful swarm intelligence metaheuristics for data classification. ACO has gained importance because of its stochastic feature and iterative adaptation procedure based on positive feedback, both of which allow for the exploration of a large area of the search space. Nevertheless, ACO also has several drawbacks that may reduce the classification accuracy and the computational time of the algorithm. This paper presents a review of related work of ACO rule classification which emphasizes the types of ACO algorithms and issues. Potential solutions that may be considered to improve the performance of ACO algorithms in the classification domain were also presented. Furthermore, this review can be used as a source of reference to other researchers in developing new ACO algorithms for rule classification.

Item Type: Article
Uncontrolled Keywords: Rule Discovery, Ant-Miner, Rule Pruning, Parameter Control, Meta heuristics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 11 Nov 2020 05:55
Last Modified: 11 Nov 2020 05:55
URI: http://repo.uum.edu.my/id/eprint/27870

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