Ant colony optimization algorithm for rule based classification: Issues and potential
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 adapta...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Little Lion Scientific
2018
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/27870/1/JTAIT%2096%2021%202018%207139%207150.pdf http://repo.uum.edu.my/27870/ http://www.jatit.org/volumes/ninetysix21.php |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | 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. |
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