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Article: Correlation kernels for support vector machines classification with applications in cancer data

TitleCorrelation kernels for support vector machines classification with applications in cancer data
Authors
Issue Date2012
PublisherHindawi Publishing Corporation. The Journal's web site is located at http://www.hindawi.com/journals/cmmm/
Citation
Computational and Mathematical Methods in Medicine, 2012, v. 2012 article no. 205025 How to Cite?
AbstractHigh dimensional bioinformatics data sets provide an excellent and challenging research problem in machine learning area. In particular, DNA microarrays generated gene expression data are of high dimension with significant level of noise. Supervised kernel learning with an SVM classifier was successfully applied in biomedical diagnosis such as discriminating different kinds of tumor tissues. Correlation Kernel has been recently applied to classification problems with Support Vector Machines (SVMs). In this paper, we develop a novel and parsimonious positive semidefinite kernel. The proposed kernel is shown experimentally to have better performance when compared to the usual correlation kernel. In addition, we propose a new kernel based on the correlation matrix incorporating techniques dealing with indefinite kernel. The resulting kernel is shown to be positive semidefinite and it exhibits superior performance to the two kernels mentioned above. We then apply the proposed method to some cancer data in discriminating different tumor tissues, providing information for diagnosis of diseases. Numerical experiments indicate that our method outperforms the existing methods such as the decision tree method and KNN method.
Persistent Identifierhttp://hdl.handle.net/10722/164178
ISSN
2015 Impact Factor: 0.887
2015 SCImago Journal Rankings: 0.415
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, Hen_US
dc.contributor.authorChing, WKen_US
dc.date.accessioned2012-09-20T07:56:18Z-
dc.date.available2012-09-20T07:56:18Z-
dc.date.issued2012en_US
dc.identifier.citationComputational and Mathematical Methods in Medicine, 2012, v. 2012 article no. 205025en_US
dc.identifier.issn1748-670X-
dc.identifier.urihttp://hdl.handle.net/10722/164178-
dc.description.abstractHigh dimensional bioinformatics data sets provide an excellent and challenging research problem in machine learning area. In particular, DNA microarrays generated gene expression data are of high dimension with significant level of noise. Supervised kernel learning with an SVM classifier was successfully applied in biomedical diagnosis such as discriminating different kinds of tumor tissues. Correlation Kernel has been recently applied to classification problems with Support Vector Machines (SVMs). In this paper, we develop a novel and parsimonious positive semidefinite kernel. The proposed kernel is shown experimentally to have better performance when compared to the usual correlation kernel. In addition, we propose a new kernel based on the correlation matrix incorporating techniques dealing with indefinite kernel. The resulting kernel is shown to be positive semidefinite and it exhibits superior performance to the two kernels mentioned above. We then apply the proposed method to some cancer data in discriminating different tumor tissues, providing information for diagnosis of diseases. Numerical experiments indicate that our method outperforms the existing methods such as the decision tree method and KNN method.-
dc.languageengen_US
dc.publisherHindawi Publishing Corporation. The Journal's web site is located at http://www.hindawi.com/journals/cmmm/-
dc.relation.ispartofComputational and Mathematical Methods in Medicineen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.meshBreast Neoplasms - metabolism-
dc.subject.meshColonic Neoplasms - metabolism-
dc.subject.meshComputational Biology - methods-
dc.subject.meshNeoplasms - pathology - therapy-
dc.subject.meshSupport Vector Machines-
dc.titleCorrelation kernels for support vector machines classification with applications in cancer dataen_US
dc.typeArticleen_US
dc.identifier.emailChing, WK: wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1155/2012/205025-
dc.identifier.pmid22919428-
dc.identifier.pmcidPMC3420228-
dc.identifier.scopuseid_2-s2.0-84866170631-
dc.identifier.hkuros206042en_US
dc.identifier.volume2012, article no. 205025en_US
dc.identifier.isiWOS:000308205900001-
dc.publisher.placeUnited Kingdom-

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