Nesting one-against-one algorithm based on SVMs for pattern classification.

Liu, B; Hao, Z; Tsang, ECC

Nesting one-against-one algorithm based on SVMs for pattern classification.

Liu, B Hao, Z Tsang, ECC

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Publication Type:: Journal Article
Citation:: IEEE Trans Neural Netw, 2008, 19 (12), pp. 2044 - 2052
Issue Date:: 2008-12

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Field	Value	Language
dc.contributor.author	Liu, B	en_US
dc.contributor.author	Hao, Z	en_US
dc.contributor.author	Tsang, ECC	en_US
dc.date.issued	2008-12	en_US
dc.identifier.citation	IEEE Trans Neural Netw, 2008, 19 (12), pp. 2044 - 2052	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15153
dc.description.abstract	Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by its decision function. This paper extends the one-against-one algorithm to handle this problem. We also give the convergence and computational complexity analysis of the proposed method. Finally, one-against-one, fuzzy decision function (FDF), and decision-directed acyclic graph (DDAG) algorithms and our proposed method are compared using five University of California at Irvine (UCI) data sets. The results report that the proposed method can handle the unclassifiable region better than others.	en_US
dc.language	eng	en_US
dc.relation.ispartof	IEEE Trans Neural Netw	en_US
dc.relation.isbasedon	10.1109/TNN.2008.2003298	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Nesting one-against-one algorithm based on SVMs for pattern classification.	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	19	en_US
utslib.location.activity	United States	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	ISI:000261544900005	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by its decision function. This paper extends the one-against-one algorithm to handle this problem. We also give the convergence and computational complexity analysis of the proposed method. Finally, one-against-one, fuzzy decision function (FDF), and decision-directed acyclic graph (DDAG) algorithms and our proposed method are compared using five University of California at Irvine (UCI) data sets. The results report that the proposed method can handle the unclassifiable region better than others.

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http://hdl.handle.net/10453/15153