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

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

5 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 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.

Original language	English
Title of host publication	Proceedings of 2011 International Conference on Machine Learning and Cybernetics
Publisher	IEEE
Pages	648-653
Number of pages	6
ISBN (Electronic)	9781457703089
ISBN (Print)	9781457703058
DOIs	https://doi.org/10.1109/ICMLC.2011.6016830
Publication status	Published - Jul 2011
Externally published	Yes
Event	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 10 Jul 2011 → 13 Jul 2011

Conference

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

Keywords

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

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10.1109/ICMLC.2011.6016830

Cite this

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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",

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doi = "10.1109/ICMLC.2011.6016830",

language = "English",

isbn = "9781457703058",

pages = "648--653",

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publisher = "IEEE",

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

}

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 of 2011 International Conference on Machine Learning and Cybernetics. IEEE, 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

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 - 2011/7

Y1 - 2011/7

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 - Conference paper (refereed)

SN - 9781457703058

SP - 648

EP - 653

BT - Proceedings of 2011 International Conference on Machine Learning and Cybernetics

PB - IEEE

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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Keywords

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