Pierola, A.; Epifanio, I.; Alemany Mut, MS. (2016). An ensemble of ordered logistic regression and random forest for child garment size matching. Computers and Industrial Engineering. 101:455-465. https://doi.org/10.1016/j.cie.2016.10.013
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/81737
Title:
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An ensemble of ordered logistic regression and random forest for child garment size matching
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Author:
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Pierola, Ana
Epifanio, I.
Alemany Mut, Mª Sandra
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UPV Unit:
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Universitat Politècnica de València. Instituto Universitario Mixto de Biomecánica de Valencia - Institut Universitari Mixt de Biomecànica de València
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Issued date:
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Abstract:
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Size 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 ...[+]
Size 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. (C) 2016 Elsevier Ltd. All rights reserved.
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Subjects:
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Multivariate conditional random forest
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Proportional odds logistic regression
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Supervised learning
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Ordinal classification
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Childrenswear garment fitting
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Variable importance
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Copyrigths:
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Cerrado |
Source:
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Computers and Industrial Engineering. (issn:
0360-8352
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DOI:
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10.1016/j.cie.2016.10.013
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Publisher:
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Elsevier
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Publisher version:
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http://dx.doi.org/10.1016/j.cie.2016.10.013
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Project ID:
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info:eu-repo/grantAgreement/MINECO//DPI2013-47279-C2-1-R/ES/HERRAMIENTAS PARA LA PREDICCION DE LA TALLA Y EL AJUSTE DE ROPA INFANTIL A PARTIR DE LA RECONSTRUCCION 3D DEL CUERPO Y DE TECNICAS BIG DATA/
info:eu-repo/grantAgreement/MINECO//DPI2013-47279-C2-2-R/ES/DESARROLLO DE UN SISTEMA DE CAPTURA 3D DEL CUERPO DEL NIÑO MEDIANTE TECNOLOGIA DOMESTICA/
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Thanks:
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This work has been partially supported by Grants DPI2013-47279-C2-1-R and DPI2013-47279-C2-2-R.
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Type:
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Artículo
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