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dc.contributor.authorPierola, A.
dc.contributor.authorEpifanio López, Irene
dc.contributor.authorAlemany Mut, Sandra
dc.date.accessioned2016-11-02T11:54:24Z
dc.date.available2016-11-02T11:54:24Z
dc.date.issued2016-11
dc.identifier.citationPIEROLA, A.; EPIFANIO, I.; ALEMANY, S. An ensemble of ordered logistic regression and random forest for child garment size matching. Computers & Industrial Engineering, 2016, vol. 101, p. 455-465.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/163969
dc.description.abstractSize fitting is a significant problem for online garment shops. The return rates due to size misfit are very high. We propose an ensemble (with an original and novel definition of the weights) of ordered logistic regression and random forest (RF) for solving the size matching problem, where ordinal data should be classified. These two classifiers are good candidates for combined use due to their complementary characteristics. A multivariate response (an ordered factor and a numeric value assessing the fit) was considered with a conditional random forest. A fit assessment study was carried out with 113 children. They were measured using a 3D body scanner to obtain their anthropometric measurements. Children tested different garments of different sizes, and their fit was assessed by an expert. Promising results have been achieved with our methodology. Two new measures have been introduced based on RF with multivariate responses to gain a better understanding of the data. One of them is an intervention in prediction measure defined locally and globally. It is shown that it is a good alternative to variable importance measures and it can be used for new observations and with multivariate responses. The other proposed tool informs us about the typicality of a case and allows us to determine archetypical observations in each class.ca_CA
dc.description.sponsorShipThis work has been partially supported by Grants DPI2013-47279-C2-1-R and DPI2013-47279-C2-2-R.ca_CA
dc.format.extent10 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherElsevierca_CA
dc.relation.isPartOfComputers & Industrial Engineering Volume 101, November 2016ca_CA
dc.rightsCopyright © 2016 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V.ca_CA
dc.subjectMultivariate conditional random forestca_CA
dc.subjectProportional odds logistic regressionca_CA
dc.subjectSupervised learningca_CA
dc.subjectOrdinal classificationca_CA
dc.subjectChildrenswear garment fittingca_CA
dc.subjectVariable importanceca_CA
dc.titleAn ensemble of ordered logistic regression and random forest for child garment size matchingca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1016/j.cie.2016.10.013
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://www.sciencedirect.com/science/article/pii/S0360835216303825ca_CA


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