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A data-driven approach to online fitting services
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.ORCID iD: 0000-0003-0274-9026
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.ORCID iD: 0000-0003-0412-6199
Univ Boras, Swedish Sch Text, Boras, Sweden.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
2018 (English)In: Data Science And Knowledge Engineering For Sensing Decision Support / [ed] Liu, J, Lu, J, Xu, Y, Martinez, L & Kerre, EE, World Scientific, 2018, Vol. 11, p. 1559-1566Conference paper, Published paper (Refereed)
Abstract [en]

Being able to accurately predict several attributes related to size is vital for services supporting online fitting. In this paper, we investigate a data-driven approach, while comparing two different supervised modeling techniques for predictive regression; standard multiple linear regression and neural networks. Using a fairly large, publicly available, data set of high quality, the main results are somewhat discouraging. Specifically, it is questionable whether key attributes like sleeve length, neck size, waist and chest can be modeled accurately enough using easily accessible input variables as sex, weight and height. This is despite the fact that several services online offer exactly this functionality. For this specific task, the results show that standard linear regression was as accurate as the potentially more powerful neural networks. Most importantly, comparing the predictions to reasonable levels for acceptable errors, it was found that an overwhelming majority of all instances had at least one attribute with an unacceptably high prediction error. In fact, if requiring that all variables are predicted with an acceptable accuracy, less than 5 % of all instances met that criterion. Specifically, for females, the success rate was as low as 1.8 %.

Place, publisher, year, edition, pages
World Scientific, 2018. Vol. 11, p. 1559-1566
Series
World Scientific Proceedings Series on Computer Engineering and Information Science ; 11
Keywords [en]
Predictive regression; online fitting; fashion
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:hj:diva-44183DOI: 10.1142/9789813273238_0194ISI: 000468160600194ISBN: 978-981-3273-24-5 (electronic)ISBN: 978-981-3273-22-1 (print)OAI: oai:DiVA.org:hj-44183DiVA, id: diva2:1322768
Conference
13th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS), Belfast, Ireland, 21-24 August, 2018
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-08-22Bibliographically approved

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Löfström, TuweJohansson, UlfSundell, Håkan
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