Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00521-003-0362-3
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dc.titleSaliency analysis of support vector machines for gene selection in tissue classification
dc.contributor.authorCao, L.
dc.contributor.authorSeng, C.K.
dc.contributor.authorGu, Q.
dc.contributor.authorLee, H.P.
dc.date.accessioned2016-11-16T11:06:04Z
dc.date.available2016-11-16T11:06:04Z
dc.date.issued2003-05
dc.identifier.citationCao, L., Seng, C.K., Gu, Q., Lee, H.P. (2003-05). Saliency analysis of support vector machines for gene selection in tissue classification. Neural Computing and Applications 11 (3-4) : 244-249. ScholarBank@NUS Repository. https://doi.org/10.1007/s00521-003-0362-3
dc.identifier.issn09410643
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/130447
dc.description.abstractThis paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for gene selection in tissue classification. The importance of genes is ranked by evaluating the sensitivity of the output to the inputs in terms of the partial derivative. A systematic learning algorithm called the Recursive Saliency Analysis (RSA) algorithm is developed to remove irrelevant genes. One simulated data and two gene expression data sets for tissue classification are evaluated in the experiment. The simulation results demonstrate that RSA is effective in SVMs for identifying important genes.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s00521-003-0362-3
dc.sourceScopus
dc.subjectFeature selection
dc.subjectSaliency analysis
dc.subjectSupport vector machines
dc.typeArticle
dc.contributor.departmentMATHEMATICS
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1007/s00521-003-0362-3
dc.description.sourcetitleNeural Computing and Applications
dc.description.volume11
dc.description.issue3-4
dc.description.page244-249
dc.identifier.isiut000184615000013
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