Improved Artificial Bee Colony Algorithm and its Application in Classification-Reference-Cited by-同舟云学术

Improved Artificial Bee Colony Algorithm and its Application in Classification

Published:2018-12-20 Issue:6 Volume:30 Page:921-926
ISSN:1883-8049
Container-title:Journal of Robotics and Mechatronics
language:en
Short-container-title:J. Robot. Mechatron.

Author:

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

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.

Publisher

Fuji Technology Press Ltd.

Subject

Electrical and Electronic Engineering,General Computer Science

Reference32 articles.

1. U. Fayyad, G. Piatetsky-Shapiro, and P. Smith, “From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining,” MIT Press, pp. 1-34, 1996.

2. F. Otero, A. Freitas, and C. Johnson, “A new sequential covering strategy for inducing classification rules with ant colony algorithms,” IEEE Trans. on Evolutionary Computation, Vol.17, No.1, pp. 64-76, 2013.

3. H. Liu and H. Motoda, “Feature selection for knowledge discovery and data mining,” Kluwer Academic, 1998.

4. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.27, No.8, pp. 1226-1238, 2005.

5. P. E. Meyer and G. Bontempi, “On the use of variable complementarity for feature selection in cancer classification,” Proc. of European workshop on applications of evolutionary computing: Evo Workshops, pp. 91-102, 2006.

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Search Method Combining Average Distance of Particle Swarm and Crowding Distance of Particles;2022 41st Chinese Control Conference (CCC);2022-07-25

2. Intelligent Parking Guidance System based on Improved Bee Colony Algorithm;2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI);2022-05-27

3. Improved artificial bee colony algorithm for air freight station scheduling;Mathematical Biosciences and Engineering;2022