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

doi:10.1109/ICAMechS.2016.7813454

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 proceeding › Conference contribution › peer-review

8 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 language	English
Title of host publication	Conference Proceedings - 2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016
Publisher	IEEE
Pages	242-247
Number of pages	6
ISBN (Electronic)	9781509053469
ISBN (Print)	978-1-5090-5347-6
DOIs	https://doi.org/10.1109/ICAMechS.2016.7813454
Publication status	Published - 16 Jan 2017
Event	2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016 - Melbourne, Australia Duration: 30 Nov 2016 → 3 Dec 2016

Conference

Conference	2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016
Country/Territory	Australia
City	Melbourne
Period	30/11/16 → 3/12/16

Keywords

Artificial bee colony algorithm
Classification
Feature selection
Support vector machines

Access to Document

10.1109/ICAMechS.2016.7813454

Cite this

@inproceedings{7e95d7bd4df441bc80c35938814467e5,

title = "Parameters optimization of classifier and feature selection based on improved artificial bee colony algorithm",

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.",

keywords = "Artificial bee colony algorithm, Classification, Feature selection, Support vector machines",

author = "Haiquan Wang and Hongnian Yu and Qian Zhang and Shuang Cang and Wudai Liao and Fanbing Zhu",

year = "2017",

month = jan,

day = "16",

doi = "10.1109/ICAMechS.2016.7813454",

language = "English",

isbn = "978-1-5090-5347-6",

pages = "242--247",

booktitle = "Conference Proceedings - 2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016",

publisher = "IEEE",

address = "United States",

note = "2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016 ; Conference date: 30-11-2016 Through 03-12-2016",

}

Wang, H, Yu, H, Zhang, Q, Cang, S, Liao, W & Zhu, F 2017, Parameters optimization of classifier and feature selection based on improved artificial bee colony algorithm. in Conference Proceedings - 2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016. IEEE, pp. 242-247, 2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016, Melbourne, Australia, 30/11/16. https://doi.org/10.1109/ICAMechS.2016.7813454

Parameters optimization of classifier and feature selection based on improved artificial bee colony algorithm. / Wang, Haiquan; Yu, Hongnian; Zhang, Qian et al.
Conference Proceedings - 2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016. IEEE, 2017. p. 242-247.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Parameters optimization of classifier and feature selection based on improved artificial bee colony algorithm

AU - Wang, Haiquan

AU - Yu, Hongnian

AU - Zhang, Qian

AU - Cang, Shuang

AU - Liao, Wudai

AU - Zhu, Fanbing

PY - 2017/1/16

Y1 - 2017/1/16

N2 - 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.

AB - 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.

KW - Artificial bee colony algorithm

KW - Classification

KW - Feature selection

KW - Support vector machines

UR - https://www.scopus.com/pages/publications/85011854036

U2 - 10.1109/ICAMechS.2016.7813454

DO - 10.1109/ICAMechS.2016.7813454

M3 - Conference contribution

SN - 978-1-5090-5347-6

SP - 242

EP - 247

BT - Conference Proceedings - 2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016

PB - IEEE

T2 - 2016 International Conference on Advanced Mechatronic Systems, ICAMechS 2016

Y2 - 30 November 2016 through 3 December 2016

ER -

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this