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JRM Vol.30 No.6 pp. 921-926
doi: 10.20965/jrm.2018.p0921
(2018)

Paper:

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Improved Artificial Bee Colony Algorithm and its Application in Classification

Haiquan Wang*, Jianhua Wei**, Shengjun Wen*, Hongnian Yu***, and Xiguang Zhang*

*Zhongyuan Petersburg Aviation College, Zhongyuan University of Technology
41 Zhongyuan Road, Zhengzhou 450007, China

**School of Electric and Information Engineering, Zhongyuan University of Technology
41 Zhongyuan Road, Zhengzhou 450007, China

***Faculty of Science and Technology, Bournemouth University
Fern Barrow, Poole, Dorset BH12 5BB, United Kingdom

Received:
May 8, 2018
Accepted:
September 26, 2018
Published:
December 20, 2018
Creative Commons License
Keywords:
artificial bee colony algorithm, classifier, SVM, UCI database, wrapper method
Abstract

For improving the classification accuracy of the classifier, a novel classification methodology based on artificial bee colony algorithm is proposed for optimal feature and SVM parameters selection. In order to balance the ability of exploration and exploitation of traditional ABC algorithm, improvements are introduced for the generation of initial solution set and onlooker bee stage. The proposed algorithm is applied to four datasets with different attribute characteristics from UCI and efficiency of the algorithm is proved from the results.

Fitness values of two optimization methods for abalone dataset

Fitness values of two optimization methods for abalone dataset

Cite this article as:
H. Wang, J. Wei, S. Wen, H. Yu, and X. Zhang, “Improved Artificial Bee Colony Algorithm and its Application in Classification,” J. Robot. Mechatron., Vol.30 No.6, pp. 921-926, 2018.
Data files:
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Last updated on Apr. 22, 2024