Self-advising support vector machine

Maali, Y; Al-Jumaily, A

Self-advising support vector machine

Maali, Y Al-Jumaily, A

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Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2013, 52 pp. 214 - 222
Issue Date:: 2013-11-01

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Field	Value	Language
dc.contributor.author	Maali, Y	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2013-11-01	en_US
dc.identifier.citation	Knowledge-Based Systems, 2013, 52 pp. 214 - 222	en_US
dc.identifier.issn	0950-7051	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27138
dc.description.abstract	The Support Vector Machine (SVM) is one of the most popular machine learning algorithms for classification and regression. SVM displays outstanding performance when utilized in many applications. However, different approaches have been proposed in order to improve its performance in general and in special cases. This paper proposes a new method of improving SVM performance in general. This method can be applied to all the types of SVMs that have differing kernel types. The key aim of the proposed approach is to transfer more information from the training phase to the testing phase. This information is obtained from the misclassified data of the training phase, which is ignored in the classic SVM. Experimental results from eleven datasets from the real online resources show that this approach can improve the classification performances of C-SVM and ν-SVM without adding any parameters to the learner algorithm. Statistical tests show significance in this improvement. The proposed method has been found to improve accuracies of C-SVM and ν-SVM in more than 67% of the experiments in which 11% of these improvements are more than 5%. It must be noted that the highest improvement found with this method was 25%. © 2013 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Knowledge-Based Systems	en_US
dc.relation.isbasedon	10.1016/j.knosys.2013.08.009	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Self-advising support vector machine	en_US
dc.type	Journal Article
utslib.citation.volume	52	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
utslib.for	17 Psychology and Cognitive Sciences	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	52	en_US

Abstract:

The Support Vector Machine (SVM) is one of the most popular machine learning algorithms for classification and regression. SVM displays outstanding performance when utilized in many applications. However, different approaches have been proposed in order to improve its performance in general and in special cases. This paper proposes a new method of improving SVM performance in general. This method can be applied to all the types of SVMs that have differing kernel types. The key aim of the proposed approach is to transfer more information from the training phase to the testing phase. This information is obtained from the misclassified data of the training phase, which is ignored in the classic SVM. Experimental results from eleven datasets from the real online resources show that this approach can improve the classification performances of C-SVM and ν-SVM without adding any parameters to the learner algorithm. Statistical tests show significance in this improvement. The proposed method has been found to improve accuracies of C-SVM and ν-SVM in more than 67% of the experiments in which 11% of these improvements are more than 5%. It must be noted that the highest improvement found with this method was 25%. © 2013 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/27138