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dc.contributor.authorEpifanio López, Irene
dc.date.accessioned2017-05-03T11:31:49Z
dc.date.available2017-05-03T11:31:49Z
dc.date.issued2017-05
dc.identifier.citationEpifanio, I. BMC Bioinformatics (2017) 18: 230. doi:10.1186/s12859-017-1650-8ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/167451
dc.description.abstractBackground Random forests are a popular method in many fields since they can be successfully applied to complex data, with a small sample size, complex interactions and correlations, mixed type predictors, etc. Furthermore, they provide variable importance measures that aid qualitative interpretation and also the selection of relevant predictors. However, most of these measures rely on the choice of a performance measure. But measures of prediction performance are not unique or there is not even a clear definition, as in the case of multivariate response random forests. Methods A new alternative importance measure, called Intervention in Prediction Measure, is investigated. It depends on the structure of the trees, without depending on performance measures. It is compared with other well-known variable importance measures in different contexts, such as a classification problem with variables of different types, another classification problem with correlated predictor variables, and problems with multivariate responses and predictors of different types. Results Several simulation studies are carried out, showing the new measure to be very competitive. In addition, it is applied in two well-known bioinformatics applications previously used in other papers. Improvements in performance are also provided for these applications by the use of this new measure. Conclusions This new measure is expressed as a percentage, which makes it attractive in terms of interpretability. It can be used with new observations. It can be defined globally, for each class (in a classification problem) and case-wise. It can easily be computed for any kind of response, including multivariate responses. Furthermore, it can be used with any algorithm employed to grow each individual tree. It can be used in place of (or in addition to) other variable importance measures.ca_CA
dc.description.sponsorShipThis work has been partially supported by Grant DPI2013- 47279-C2-1- R from the Spanish Ministerio de Economía y Competitividad. The funders played no role in the design or conclusions of this study.ca_CA
dc.format.extent16 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringer Verlagca_CA
dc.publisherBioMed Centralca_CA
dc.relation.isPartOfBMC Bioinformatics (2017) 18:230ca_CA
dc.rights© The Author(s) 2017ca_CA
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectrandom forestca_CA
dc.subjectvariable importance measureca_CA
dc.subjectmultivariate responseca_CA
dc.subjectfeature selectionca_CA
dc.subjectconditional inference treesca_CA
dc.titleIntervention in prediction measure: a new approach to assessing variable importance for random forestsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1186/s12859-017-1650-8
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://link.springer.com/article/10.1186/s12859-017-1650-8ca_CA


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