Parameters optimization of classifier and feature selection based on improved artificial bee colony algorithm

Wang, Haiquan, Yu, Hongnian, Zhang, Qian, Cang, Shuang, Liao, Wudai and Zhu, Fanbing (2017) Parameters optimization of classifier and feature selection based on improved artificial bee colony algorithm. In: 2016 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, pp. 242-247. ISBN 978-1-5090-5347-6

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Abstract

The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm, the initialization and scout bee phase are improved. To evaluate the proposed approach, the simulation was executed based on datasets from the UCI database. The effectiveness of the proposed method is confirmed by simulation results.

Item Type:	Book Section
Uncontrolled Keywords:	Classification, Feature selection, Support vector machines, Artificial bee colony algorithm
Subjects:	P900 Others in Mass Communications and Documentation
Department:	Faculties > Business and Law > Newcastle Business School
Depositing User:	Becky Skoyles
Date Deposited:	30 Nov 2018 12:39
Last Modified:	19 Nov 2019 09:48
URI:	http://nrl.northumbria.ac.uk/id/eprint/36995

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