Robust Support Vector Machine

Trung Le; Dat Tran; Wanli Ma; Thien Pham; Phuong Duong; Minh Nguyen

doi:10.1109/IJCNN.2014.6889587

Robust Support Vector Machine

Trung Le, Dat Tran, Wanli Ma, Thien Pham, Phuong Duong, Minh Nguyen

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

9 Citations (Scopus)

Abstract

Support Vector Machine (SVM) is a well-known kernel-based method for binary classification problem. SVM aims at constructing the optimal middle hyperplane which induces the largest margin. It is proven that in a linearly separable case, this middle hyperplane offers the high accuracy on universal datasets. However, real world datasets often contain overlapping regions and therefore, the decision hyperplane should be adjusted according to the profiles of the datasets. In this paper, we propose Robust Support Vector Machine (RSVM), where the hyperplanes can be properly adjusted to accommodate the real world datasets. By setting the value of the adjustment factor properly, RSVM can handle well the datasets with any possible profiles. Our experiments on the benchmark datasets demonstrate the superiority of the RSVM for both binary and one-class classification problems.

Original language	English
Title of host publication	Proceedings of the 2014 International Joint Conference on Neural Networks
Editors	Cesare Alippi
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	4137-4144
Number of pages	8
ISBN (Electronic)	9781479914845, 9781479966271
ISBN (Print)	9781479914821
DOIs	https://doi.org/10.1109/IJCNN.2014.6889587
Publication status	Published - 2014
Externally published	Yes
Event	IEEE International Joint Conference on Neural Networks 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 https://ieeexplore.ieee.org/xpl/conhome/6880678/proceeding (Proceedings)

Conference

Conference	IEEE International Joint Conference on Neural Networks 2014
Abbreviated title	IJCNN 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6880678/proceeding (Proceedings)

Keywords

Kernel-based method
One-class Support Vector Machine
Support Vector Machine

Access to Document

10.1109/IJCNN.2014.6889587

Cite this

@inproceedings{76811bf28eae462aaa5421ef49545a1c,

title = "Robust Support Vector Machine",

abstract = "Support Vector Machine (SVM) is a well-known kernel-based method for binary classification problem. SVM aims at constructing the optimal middle hyperplane which induces the largest margin. It is proven that in a linearly separable case, this middle hyperplane offers the high accuracy on universal datasets. However, real world datasets often contain overlapping regions and therefore, the decision hyperplane should be adjusted according to the profiles of the datasets. In this paper, we propose Robust Support Vector Machine (RSVM), where the hyperplanes can be properly adjusted to accommodate the real world datasets. By setting the value of the adjustment factor properly, RSVM can handle well the datasets with any possible profiles. Our experiments on the benchmark datasets demonstrate the superiority of the RSVM for both binary and one-class classification problems.",

keywords = "Kernel-based method, One-class Support Vector Machine, Support Vector Machine",

author = "Trung Le and Dat Tran and Wanli Ma and Thien Pham and Phuong Duong and Minh Nguyen",

year = "2014",

doi = "10.1109/IJCNN.2014.6889587",

language = "English",

isbn = "9781479914821",

pages = "4137--4144",

editor = "Cesare Alippi",

booktitle = "Proceedings of the 2014 International Joint Conference on Neural Networks",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Joint Conference on Neural Networks 2014, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

url = "https://ieeexplore.ieee.org/xpl/conhome/6880678/proceeding",

}

Le, T, Tran, D, Ma, W, Pham, T, Duong, P & Nguyen, M 2014, Robust Support Vector Machine. in C Alippi (ed.), Proceedings of the 2014 International Joint Conference on Neural Networks. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 4137-4144, IEEE International Joint Conference on Neural Networks 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/IJCNN.2014.6889587

TY - GEN

T1 - Robust Support Vector Machine

AU - Le, Trung

AU - Tran, Dat

AU - Ma, Wanli

AU - Pham, Thien

AU - Duong, Phuong

AU - Nguyen, Minh

PY - 2014

Y1 - 2014

N2 - Support Vector Machine (SVM) is a well-known kernel-based method for binary classification problem. SVM aims at constructing the optimal middle hyperplane which induces the largest margin. It is proven that in a linearly separable case, this middle hyperplane offers the high accuracy on universal datasets. However, real world datasets often contain overlapping regions and therefore, the decision hyperplane should be adjusted according to the profiles of the datasets. In this paper, we propose Robust Support Vector Machine (RSVM), where the hyperplanes can be properly adjusted to accommodate the real world datasets. By setting the value of the adjustment factor properly, RSVM can handle well the datasets with any possible profiles. Our experiments on the benchmark datasets demonstrate the superiority of the RSVM for both binary and one-class classification problems.

AB - Support Vector Machine (SVM) is a well-known kernel-based method for binary classification problem. SVM aims at constructing the optimal middle hyperplane which induces the largest margin. It is proven that in a linearly separable case, this middle hyperplane offers the high accuracy on universal datasets. However, real world datasets often contain overlapping regions and therefore, the decision hyperplane should be adjusted according to the profiles of the datasets. In this paper, we propose Robust Support Vector Machine (RSVM), where the hyperplanes can be properly adjusted to accommodate the real world datasets. By setting the value of the adjustment factor properly, RSVM can handle well the datasets with any possible profiles. Our experiments on the benchmark datasets demonstrate the superiority of the RSVM for both binary and one-class classification problems.

KW - Kernel-based method

KW - One-class Support Vector Machine

KW - Support Vector Machine

UR - http://www.scopus.com/inward/record.url?scp=84908491032&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2014.6889587

DO - 10.1109/IJCNN.2014.6889587

M3 - Conference Paper

AN - SCOPUS:84908491032

SN - 9781479914821

SP - 4137

EP - 4144

BT - Proceedings of the 2014 International Joint Conference on Neural Networks

A2 - Alippi, Cesare

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE International Joint Conference on Neural Networks 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

Robust Support Vector Machine

Abstract

Conference

Keywords

Access to Document

Other files and links

Cite this