Artificial bee colony based data mining algorithms for classification tasks

Mohd Syukran, Mohd Afizi and Yuk, Ying Chung and Wei-Chang, Yeh and Wahid, Noorhaniza and Ahmad Zaidi , Ahmad Mujahid (2011) Artificial bee colony based data mining algorithms for classification tasks. Modern Applied Science, 5 (4). pp. 217-231.

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Abstract

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.

Item Type:Article
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Faculty of Computer Science and Information Technology > Department of Information Security
ID Code:3035
Deposited By:Normajihan Abd. Rahman
Deposited On:20 Feb 2013 15:14
Last Modified:22 Jan 2015 08:38

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