Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN

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dc.authoridOztaysi, Basar / 0000-0002-1090-7963
dc.authoridDOGAN, ONUR / 0000-0003-3543-4012
dc.authorscopusid57202924825
dc.authorscopusid8572344300
dc.authorwosidOztaysi, Basar/K-7498-2013
dc.authorwosidDogan, Onur/AAN-3208-2021
dc.authorwosidDOGAN, ONUR/ABI-4575-2020
dc.contributor.authorDoğan, Onur
dc.contributor.authorÖztayşi, Başar
dc.date.accessioned2022-02-15T16:57:34Z
dc.date.available2022-02-15T16:57:34Z
dc.date.issued2019
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractCompanies have an advantage over the competitors if they can present customized offers to customers. Demographic information of customers is critical for the companies to develop individualized systems. While current technologies make it easy to collect customer data, the main problem is that demographic data are usually incomplete. Hence, several methods are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy sets. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the research is to classify customer data into the gender classes using indoor paths. (C) 2019 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2019.06.029
dc.identifier.endpage49en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85067416246en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage42en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2019.06.029
dc.identifier.urihttps://hdl.handle.net/20.500.14034/210
dc.identifier.volume136en_US
dc.identifier.wosWOS:000484871300004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.journalExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGender predictionen_US
dc.subjectPath predictionen_US
dc.subjectFuzzy setsen_US
dc.subjectFuzzy kNNen_US
dc.subjectIndoor pathsen_US
dc.subjectLevenshtein distancesen_US
dc.subjectRecognitionen_US
dc.titleGenders prediction from indoor customer paths by Levenshtein-based fuzzy kNNen_US
dc.typeArticleen_US

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