A new method for multi-class support vector machines by training least number of classifiers

Ran Wang; Sam Kwong; De-Gang Chen

doi:10.1109/ICMLC.2011.6016830

A new method for multi-class support vector machines by training least number of classifiers

Ran Wang
, Sam Kwong
, De-Gang Chen

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

How to well apply Support Vector Machine (SVM) technique to multi-class classification problem is an important topic in the area of machine learning. In this paper, we propose a novel method which is different from all the existing ones. By constructing the least number of classifiers, it makes better use of the feature space partition, and can fully eliminate the unclassifiable region. The method is specially designed for 2^k-class problems first and could be possibly extended further. We compare the proposed method with several existing ones as one-against-rest (OAR), one-against-one (OAO), decision directed acyclic graph (DDAG), and decision tree (DT) based architecture. Experimental results exhibit good feasibility of the proposed model in term of generalization capability, training time and testing time. © 2011 IEEE.

Original language	English
Title of host publication	Proceedings - International Conference on Machine Learning and Cybernetics
Pages	648-653
Volume	2
DOIs	https://doi.org/10.1109/ICMLC.2011.6016830
Publication status	Published - 2011
Event	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 10 Jul 2011 → 13 Jul 2011

Publication series

Name
Volume	2
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Place	China
City	Guilin, Guangxi
Period	10/07/11 → 13/07/11

Research Keywords

Hyper-plane
Multi-class classification
Support vector machine
Unclassifiable region

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10.1109/ICMLC.2011.6016830

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title = "A new method for multi-class support vector machines by training least number of classifiers",

abstract = "How to well apply Support Vector Machine (SVM) technique to multi-class classification problem is an important topic in the area of machine learning. In this paper, we propose a novel method which is different from all the existing ones. By constructing the least number of classifiers, it makes better use of the feature space partition, and can fully eliminate the unclassifiable region. The method is specially designed for 2k-class problems first and could be possibly extended further. We compare the proposed method with several existing ones as one-against-rest (OAR), one-against-one (OAO), decision directed acyclic graph (DDAG), and decision tree (DT) based architecture. Experimental results exhibit good feasibility of the proposed model in term of generalization capability, training time and testing time. {\textcopyright} 2011 IEEE.",

keywords = "Hyper-plane, Multi-class classification, Support vector machine, Unclassifiable region",

author = "Ran Wang and Sam Kwong and De-Gang Chen",

year = "2011",

doi = "10.1109/ICMLC.2011.6016830",

language = "English",

isbn = "9781457703065",

volume = "2",

pages = "648--653",

booktitle = "Proceedings - International Conference on Machine Learning and Cybernetics",

note = "2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 ; Conference date: 10-07-2011 Through 13-07-2011",

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Wang, R, Kwong, S & Chen, D-G 2011, A new method for multi-class support vector machines by training least number of classifiers. in Proceedings - International Conference on Machine Learning and Cybernetics. vol. 2, 6016830, pp. 648-653, 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, Guangxi, China, 10/07/11. https://doi.org/10.1109/ICMLC.2011.6016830

A new method for multi-class support vector machines by training least number of classifiers. / Wang, Ran; Kwong, Sam; Chen, De-Gang.
Proceedings - International Conference on Machine Learning and Cybernetics. Vol. 2 2011. p. 648-653 6016830.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A new method for multi-class support vector machines by training least number of classifiers

AU - Wang, Ran

AU - Kwong, Sam

AU - Chen, De-Gang

PY - 2011

Y1 - 2011

N2 - How to well apply Support Vector Machine (SVM) technique to multi-class classification problem is an important topic in the area of machine learning. In this paper, we propose a novel method which is different from all the existing ones. By constructing the least number of classifiers, it makes better use of the feature space partition, and can fully eliminate the unclassifiable region. The method is specially designed for 2k-class problems first and could be possibly extended further. We compare the proposed method with several existing ones as one-against-rest (OAR), one-against-one (OAO), decision directed acyclic graph (DDAG), and decision tree (DT) based architecture. Experimental results exhibit good feasibility of the proposed model in term of generalization capability, training time and testing time. © 2011 IEEE.

AB - How to well apply Support Vector Machine (SVM) technique to multi-class classification problem is an important topic in the area of machine learning. In this paper, we propose a novel method which is different from all the existing ones. By constructing the least number of classifiers, it makes better use of the feature space partition, and can fully eliminate the unclassifiable region. The method is specially designed for 2k-class problems first and could be possibly extended further. We compare the proposed method with several existing ones as one-against-rest (OAR), one-against-one (OAO), decision directed acyclic graph (DDAG), and decision tree (DT) based architecture. Experimental results exhibit good feasibility of the proposed model in term of generalization capability, training time and testing time. © 2011 IEEE.

KW - Hyper-plane

KW - Multi-class classification

KW - Support vector machine

KW - Unclassifiable region

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U2 - 10.1109/ICMLC.2011.6016830

DO - 10.1109/ICMLC.2011.6016830

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781457703065

VL - 2

SP - 648

EP - 653

BT - Proceedings - International Conference on Machine Learning and Cybernetics

T2 - 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011

Y2 - 10 July 2011 through 13 July 2011

ER -

A new method for multi-class support vector machines by training least number of classifiers

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