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QuantificationLib: A Python library for quantification and prevalence estimation

dc.contributor.authorCastaño Gutiérrez, Alberto 
dc.contributor.authorAlonso González, Jaime 
dc.contributor.authorGonzález González, Pablo 
dc.contributor.authorPérez Núñez, Pablo 
dc.contributor.authorCoz Velasco, Juan José del 
dc.date.accessioned2024-04-19T07:14:28Z
dc.date.available2024-04-19T07:14:28Z
dc.date.issued2024-05
dc.identifier.citationSoftwareX, 26, p. 101728 (2024); doi: 10.1016/j.softx.2024.101728
dc.identifier.issn2352-7110
dc.identifier.urihttps://hdl.handle.net/10651/72372
dc.description.abstractQuantificationLib is an open-source Python library that provides a comprehensive set of algorithms for quantification learning. Quantification, also known as prevalence estimation, is a supervised machine-learning task where the objective is to train a model that is able to predict the distribution of classes in a set of unseen examples or bags. This library offers a wide variety of quantification methods suited for easy prototyping and experimentation, applicable to a wide range of quantification applications.spa
dc.description.sponsorshipThis work was supported by grant PID2019-110742RB-I00 from Spanish Ministerio de Economía y Competitividad (MINECO) and grant PID2019-109238GB-C21 from Spanish Ministry of Science and Innovation.spa
dc.format.extentp. 101728spa
dc.language.isoengspa
dc.publisherElsevierspa
dc.relation.ispartofSoftwareX, 26, p. 101728 (2024); doi: 10.1016/j.softx.2024.101728spa
dc.rightsAtribución 4.0 Internacional*
dc.rights© The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectQuantification learningspa
dc.subjectPrevalence estimationspa
dc.subjectOrdinal quantificationspa
dc.titleQuantificationLib: A Python library for quantification and prevalence estimationspa
dc.typejournal articlespa
dc.identifier.doi10.1016/j.softx.2024.101728
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110742RB-I00/ES/EXPLOTANDO EL CONOCIMIENTO DISPONIBLE PARA ADAPTAR Y APLICAR MODELOS APRENDIDOS A PARTIR DE DOMINIOS DIFERENTES/ spa
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C21/ES/SISTEMAS DE RECOMENDACION EXPLICABLES/ spa
dc.relation.publisherversionhttps://doi.org/10.1016/j.softx.2024.101728spa
dc.rights.accessRightsopen accessspa
dc.type.hasVersionVoRspa


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