Parameter determination of support vector machine using scatter search approach

Mohammed H. Afif; Abdel Rahman Hedar; Taysir H.Abdel Hamid; Yousef B. Mahdy

doi:10.1109/ICCTA.2012.6523566

Parameter determination of support vector machine using scatter search approach

Mohammed H. Afif
, Abdel Rahman Hedar
, Taysir H.Abdel Hamid
, Yousef B. Mahdy

Assiut University

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

Abstract

Support Vector Machine (SVM) is a popular data classification method with many diverse applications. SVM has many parameters, which have significant influences on the performance of SVM classifier. In this paper, a Scatter Search approach is used to find near optimal values of the SVM parameters and its kernel parameters. The proposed method integrates a scatter search approach with support vector machine using three different kernel functions, shortly (3SVM). To evaluate the performance of the proposed method, 4 benchmark datasets are used. Experiments and comparisons prove that the 3SVM is a promising approach and has a competitive performance relative to some other published methods.

Original language	English
Title of host publication	2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012
Pages	181-186
Number of pages	6
DOIs	https://doi.org/10.1109/ICCTA.2012.6523566
State	Published - 2012
Externally published	Yes
Event	2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012 - Alexandria, Egypt Duration: 13 Oct 2012 → 15 Oct 2012

Publication series

Name	2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012

Conference

Conference	2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012
Country/Territory	Egypt
City	Alexandria
Period	13/10/12 → 15/10/12

Access to Document

10.1109/ICCTA.2012.6523566

Cite this

Afif, M. H., Hedar, A. R., Hamid, T. H. A., & Mahdy, Y. B. (2012). Parameter determination of support vector machine using scatter search approach. In 2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012 (pp. 181-186). Article 6523566 (2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012). https://doi.org/10.1109/ICCTA.2012.6523566

@inproceedings{cccd6a420aca452bb8175c97b916f73d,

title = "Parameter determination of support vector machine using scatter search approach",

abstract = "Support Vector Machine (SVM) is a popular data classification method with many diverse applications. SVM has many parameters, which have significant influences on the performance of SVM classifier. In this paper, a Scatter Search approach is used to find near optimal values of the SVM parameters and its kernel parameters. The proposed method integrates a scatter search approach with support vector machine using three different kernel functions, shortly (3SVM). To evaluate the performance of the proposed method, 4 benchmark datasets are used. Experiments and comparisons prove that the 3SVM is a promising approach and has a competitive performance relative to some other published methods.",

author = "Afif, \{Mohammed H.\} and Hedar, \{Abdel Rahman\} and Hamid, \{Taysir H.Abdel\} and Mahdy, \{Yousef B.\}",

year = "2012",

doi = "10.1109/ICCTA.2012.6523566",

language = "English",

isbn = "9781467328234",

series = "2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012",

pages = "181--186",

booktitle = "2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012",

note = "2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012 ; Conference date: 13-10-2012 Through 15-10-2012",

}

Afif, MH, Hedar, AR, Hamid, THA & Mahdy, YB 2012, Parameter determination of support vector machine using scatter search approach. in 2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012., 6523566, 2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012, pp. 181-186, 2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012, Alexandria, Egypt, 13/10/12. https://doi.org/10.1109/ICCTA.2012.6523566

Parameter determination of support vector machine using scatter search approach. / Afif, Mohammed H.; Hedar, Abdel Rahman; Hamid, Taysir H.Abdel et al.
2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012. 2012. p. 181-186 6523566 (2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012).

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

TY - GEN

T1 - Parameter determination of support vector machine using scatter search approach

AU - Afif, Mohammed H.

AU - Hedar, Abdel Rahman

AU - Hamid, Taysir H.Abdel

AU - Mahdy, Yousef B.

PY - 2012

Y1 - 2012

N2 - Support Vector Machine (SVM) is a popular data classification method with many diverse applications. SVM has many parameters, which have significant influences on the performance of SVM classifier. In this paper, a Scatter Search approach is used to find near optimal values of the SVM parameters and its kernel parameters. The proposed method integrates a scatter search approach with support vector machine using three different kernel functions, shortly (3SVM). To evaluate the performance of the proposed method, 4 benchmark datasets are used. Experiments and comparisons prove that the 3SVM is a promising approach and has a competitive performance relative to some other published methods.

AB - Support Vector Machine (SVM) is a popular data classification method with many diverse applications. SVM has many parameters, which have significant influences on the performance of SVM classifier. In this paper, a Scatter Search approach is used to find near optimal values of the SVM parameters and its kernel parameters. The proposed method integrates a scatter search approach with support vector machine using three different kernel functions, shortly (3SVM). To evaluate the performance of the proposed method, 4 benchmark datasets are used. Experiments and comparisons prove that the 3SVM is a promising approach and has a competitive performance relative to some other published methods.

UR - https://www.scopus.com/pages/publications/84879750010

U2 - 10.1109/ICCTA.2012.6523566

DO - 10.1109/ICCTA.2012.6523566

M3 - Conference contribution

AN - SCOPUS:84879750010

SN - 9781467328234

T3 - 2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012

SP - 181

EP - 186

BT - 2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012

T2 - 2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012

Y2 - 13 October 2012 through 15 October 2012

ER -

Parameter determination of support vector machine using scatter search approach

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this