Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy

Haochen Shi; Haipeng Xiao; Jianjiang Zhou; Ning Li; Huiyu Zhou

doi:10.1109/ICSAI.2018.8599461

Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy

Haochen Shi, Haipeng Xiao, Jianjiang Zhou, Ning Li^*, Huiyu Zhou

^*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

By analyzing the influences of kernel parameter and penalty factor for generalization performance on Support Vector Machine (SVM), a novel parameter optimization algorithm based on segmented dichotomy is proposed for Radial Basis Function (RBF) kernel. Combine with Segmented Dichotomy(SD) and Gird Searching(GS) method, a composite parameter selection, SD-GS algorithm, is structured for rapid optimization of kernel parameter and penalty factor. UCI Machine Learning database is used to test our proposed method. Experimental results have shown that performance on parameter selection is better than traversal exponential grid searching. Thus, the optimized parameter combination of SD-GS algorithm enables RBF kernel in SVM to have higher generalization performance.

Original language	English
Title of host publication	2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	383-388
Number of pages	6
ISBN (Electronic)	9781728101200
DOIs	https://doi.org/10.1109/ICSAI.2018.8599461
Publication status	Published - 03 Jan 2019
Externally published	Yes
Event	5th International Conference on Systems and Informatics, ICSAI 2018 - Nanjing, China Duration: 10 Nov 2018 → 12 Nov 2018

Publication series

Name	2018 5th International Conference on Systems and Informatics, ICSAI 2018

Conference

Conference	5th International Conference on Systems and Informatics, ICSAI 2018
Country/Territory	China
City	Nanjing
Period	10/11/2018 → 12/11/2018

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (No 20175152036). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education.

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

parameter optimization
RBF kernel
segmented dichotomy(SD)
Support Vector Machine (SVM)

ASJC Scopus subject areas

Hardware and Architecture
Computer Networks and Communications
Energy Engineering and Power Technology
Control and Systems Engineering

Access to Document

10.1109/ICSAI.2018.8599461Licence: Unspecified

Cite this

Shi, H., Xiao, H., Zhou, J., Li, N., & Zhou, H. (2019). Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy. In 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings (pp. 383-388). Article 8599461 (2018 5th International Conference on Systems and Informatics, ICSAI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSAI.2018.8599461

Shi, Haochen ; Xiao, Haipeng ; Zhou, Jianjiang et al. / Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy. 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 383-388 (2018 5th International Conference on Systems and Informatics, ICSAI 2018).

@inproceedings{dc92d0c26ffa4ad4ad5e93b6f6333c2c,

title = "Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy",

abstract = "By analyzing the influences of kernel parameter and penalty factor for generalization performance on Support Vector Machine (SVM), a novel parameter optimization algorithm based on segmented dichotomy is proposed for Radial Basis Function (RBF) kernel. Combine with Segmented Dichotomy(SD) and Gird Searching(GS) method, a composite parameter selection, SD-GS algorithm, is structured for rapid optimization of kernel parameter and penalty factor. UCI Machine Learning database is used to test our proposed method. Experimental results have shown that performance on parameter selection is better than traversal exponential grid searching. Thus, the optimized parameter combination of SD-GS algorithm enables RBF kernel in SVM to have higher generalization performance.",

keywords = "parameter optimization, RBF kernel, segmented dichotomy(SD), Support Vector Machine (SVM)",

author = "Haochen Shi and Haipeng Xiao and Jianjiang Zhou and Ning Li and Huiyu Zhou",

note = "Funding Information: ACKNOWLEDGMENT This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (No 20175152036). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education. Publisher Copyright: {\textcopyright} 2018 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 5th International Conference on Systems and Informatics, ICSAI 2018 ; Conference date: 10-11-2018 Through 12-11-2018",

year = "2019",

month = jan,

day = "3",

doi = "10.1109/ICSAI.2018.8599461",

language = "English",

series = "2018 5th International Conference on Systems and Informatics, ICSAI 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "383--388",

booktitle = "2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings",

address = "United States",

}

Shi, H, Xiao, H, Zhou, J, Li, N & Zhou, H 2019, Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy. in 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings., 8599461, 2018 5th International Conference on Systems and Informatics, ICSAI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 383-388, 5th International Conference on Systems and Informatics, ICSAI 2018, Nanjing, China, 10/11/2018. https://doi.org/10.1109/ICSAI.2018.8599461

Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy. / Shi, Haochen; Xiao, Haipeng; Zhou, Jianjiang et al.
2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 383-388 8599461 (2018 5th International Conference on Systems and Informatics, ICSAI 2018).

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

TY - GEN

T1 - Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy

AU - Shi, Haochen

AU - Xiao, Haipeng

AU - Zhou, Jianjiang

AU - Li, Ning

AU - Zhou, Huiyu

N1 - Funding Information: ACKNOWLEDGMENT This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (No 20175152036). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education. Publisher Copyright: © 2018 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2019/1/3

Y1 - 2019/1/3

N2 - By analyzing the influences of kernel parameter and penalty factor for generalization performance on Support Vector Machine (SVM), a novel parameter optimization algorithm based on segmented dichotomy is proposed for Radial Basis Function (RBF) kernel. Combine with Segmented Dichotomy(SD) and Gird Searching(GS) method, a composite parameter selection, SD-GS algorithm, is structured for rapid optimization of kernel parameter and penalty factor. UCI Machine Learning database is used to test our proposed method. Experimental results have shown that performance on parameter selection is better than traversal exponential grid searching. Thus, the optimized parameter combination of SD-GS algorithm enables RBF kernel in SVM to have higher generalization performance.

AB - By analyzing the influences of kernel parameter and penalty factor for generalization performance on Support Vector Machine (SVM), a novel parameter optimization algorithm based on segmented dichotomy is proposed for Radial Basis Function (RBF) kernel. Combine with Segmented Dichotomy(SD) and Gird Searching(GS) method, a composite parameter selection, SD-GS algorithm, is structured for rapid optimization of kernel parameter and penalty factor. UCI Machine Learning database is used to test our proposed method. Experimental results have shown that performance on parameter selection is better than traversal exponential grid searching. Thus, the optimized parameter combination of SD-GS algorithm enables RBF kernel in SVM to have higher generalization performance.

KW - parameter optimization

KW - RBF kernel

KW - segmented dichotomy(SD)

KW - Support Vector Machine (SVM)

UR - http://www.scopus.com/inward/record.url?scp=85061493324&partnerID=8YFLogxK

U2 - 10.1109/ICSAI.2018.8599461

DO - 10.1109/ICSAI.2018.8599461

M3 - Conference contribution

AN - SCOPUS:85061493324

T3 - 2018 5th International Conference on Systems and Informatics, ICSAI 2018

SP - 383

EP - 388

BT - 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Systems and Informatics, ICSAI 2018

Y2 - 10 November 2018 through 12 November 2018

ER -

Shi H, Xiao H, Zhou J, Li N, Zhou H. Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy. In 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 383-388. 8599461. (2018 5th International Conference on Systems and Informatics, ICSAI 2018). doi: 10.1109/ICSAI.2018.8599461

Radial Basis Function Kernel Parameter Optimization Algorithm in Support Vector Machine Based on Segmented Dichotomy

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this