Artificial bee colony based data mining algorithms for classification tasks
Artificial Bee Colony (ABC) algorithm is considered new and widely used in searching for optimum solutions. This due to its uniqueness in problem-solving method where the solution for a problem emerges from intelligent behaviour of honeybee swarms. This paper proposes the use of the ABC algorithm as...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Canadian Center of Science and Education
2011
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3035/ |
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| Summary: | Artificial Bee Colony (ABC) algorithm is considered new and widely used in searching for optimum solutions. This due to its uniqueness in problem-solving method where the solution for a problem emerges from intelligent behaviour of honeybee swarms. This paper proposes the use of the ABC algorithm as a new tool for Data Mining particularly in classification tasks. Moreover, the proposed ABC for Data Mining were implemented and tested against six traditional classification algorithms classifiers. From the obtained results, ABC proved to be suitable candidate for classification tasks. This can be proved the experiments result where the performance of the proposed ABC algorithm has been tested by doing the experiments using UCI datasets. The results obtained in these experiments indicates that ABC algorithm are competitive, not only with other evolutionary techniques, but also to industry standard algorithms such as PAT, SOM, Naive Bayes, Classification Tree and Nearest (kNN), and can be successfully applied to more demanding problem domains. |
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