SVM-RFE: selection and visualization of the most relevant features through non-linear kernels

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• dc.contributor.author Sanz, Hector
• dc.contributor.author Valim, Clarissa
• dc.contributor.author Vegas Lozano, Esteban
• dc.contributor.author Oller, Josep M.
• dc.contributor.author Reverter, Ferran
• dc.date.accessioned 2019-05-31T07:29:49Z
• dc.date.available 2019-05-31T07:29:49Z
• dc.date.issued 2018
• dc.description.abstract Background: Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance of predictor variables. Creating predictor models based on only the most relevant variables is essential in biomedical research. Currently, substantial work has been done to allow assessment of variable importance in SVM models but this work has focused on SVM implemented with linear kernels. The power of SVM as a prediction model is associated with the flexibility generated by use of non-linear kernels. Moreover, SVM has been extended to model survival outcomes. This paper extends the Recursive Feature Elimination (RFE) algorithm by proposing three approaches to rank variables based on non-linear SVM and SVM for survival analysis. Results: The proposed algorithms allows visualization of each one the RFE iterations, and hence, identification of the most relevant predictors of the response variable. Using simulation studies based on time-to-event outcomes and three real datasets, we evaluate the three methods, based on pseudo-samples and kernel principal component analysis, and compare them with the original SVM-RFE algorithm for non-linear kernels. The three algorithms we proposed performed generally better than the gold standard RFE for non-linear kernels, when comparing the truly most relevant variables with the variable ranks produced by each algorithm in simulation studies. Generally, the RFE-pseudo-samples outperformed the other three methods, even when variables were assumed to be correlated in all tested scenarios. Conclusions: The proposed approaches can be implemented with accuracy to select variables and assess direction and strength of associations in analysis of biomedical data using SVM for categorical or time-to-event responses. Conducting variable selection and interpreting direction and strength of associations between predictors and outcomes with the proposed approaches, particularly with the RFE-pseudo-samples approach can be implemented with accuracy when analyzing biomedical data. These approaches, perform better than the classical RFE of Guyon for realistic scenarios about the structure of biomedical data.
• dc.description.sponsorship This work was funded by Grant MTM2015–64465-C2–1-R (MINECO/FEDER) from the Ministerio de Economía y Competitividad (Spain) to JMO and EV. The funding didn’t play any role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
• dc.format.mimetype application/pdf
• dc.identifier.citation Sanz H, Valim C, Vegas E, Oller JM, Reverter F. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. BMC Bioinformatics. 2018; 19(1):432. DOI 10.1186/s12859-018-2451-4
• dc.identifier.doi http://dx.doi.org/10.1186/s12859-018-2451-4
• dc.identifier.issn 1471-2105
• dc.identifier.uri http://hdl.handle.net/10230/41678
• dc.language.iso eng
• dc.publisher BioMed Central
• dc.relation.ispartof BMC Bioinformatics. 2018; 19(1):432
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/MTM2015–64465-C2–1-R
• dc.rights © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Kernel methods
• dc.subject.keyword Recursive feature elimination
• dc.subject.keyword Relevant variables
• dc.subject.keyword Support vector machines
• dc.title SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion