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

Haiquan Wang, Hongnian Yu, Qian Zhang, Shuang Cang, Wudai Liao, Fanbing Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationConference Proceedings - 2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016
PublisherIEEE
Pages242-247
Number of pages6
ISBN (Electronic)9781509053469
ISBN (Print)978-1-5090-5347-6
DOIs
Publication statusPublished - 16 Jan 2017
Event2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016 - Melbourne, Australia
Duration: 30 Nov 20163 Dec 2016

Conference

Conference2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016
CountryAustralia
CityMelbourne
Period30/11/163/12/16

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