QuantificationLib: A Python library for quantification and prevalence estimation
| dc.contributor.author | Castaño Gutiérrez, Alberto | |
| dc.contributor.author | Alonso González, Jaime | |
| dc.contributor.author | González González, Pablo | |
| dc.contributor.author | Pérez Núñez, Pablo | |
| dc.contributor.author | Coz Velasco, Juan José del | |
| dc.date.accessioned | 2024-04-19T07:14:28Z | |
| dc.date.available | 2024-04-19T07:14:28Z | |
| dc.date.issued | 2024-05 | |
| dc.identifier.citation | SoftwareX, 26, p. 101728 (2024); doi: 10.1016/j.softx.2024.101728 | |
| dc.identifier.issn | 2352-7110 | |
| dc.identifier.uri | https://hdl.handle.net/10651/72372 | |
| dc.description.abstract | QuantificationLib 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.sponsorship | This 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.extent | p. 101728 | spa |
| dc.language.iso | eng | spa |
| dc.publisher | Elsevier | spa |
| dc.relation.ispartof | SoftwareX, 26, p. 101728 (2024); doi: 10.1016/j.softx.2024.101728 | spa |
| dc.rights | Atribución 4.0 Internacional | * |
| dc.rights | © The Authors | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Quantification learning | spa |
| dc.subject | Prevalence estimation | spa |
| dc.subject | Ordinal quantification | spa |
| dc.title | QuantificationLib: A Python library for quantification and prevalence estimation | spa |
| dc.type | journal article | spa |
| dc.identifier.doi | 10.1016/j.softx.2024.101728 | |
| dc.relation.projectID | info: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.projectID | info: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.publisherversion | https://doi.org/10.1016/j.softx.2024.101728 | spa |
| dc.rights.accessRights | open access | spa |
| dc.type.hasVersion | VoR | spa |
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