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A review onquantification learning
dc.contributor.author | González González, Pablo | |
dc.contributor.author | Castaño Gutiérrez, Alberto | |
dc.contributor.author | Chawla, N. V. | |
dc.contributor.author | Coz Velasco, Juan José del | |
dc.date.accessioned | 2018-02-06T10:10:55Z | |
dc.date.available | 2018-02-06T10:10:55Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | ACM Computing Surveys, 50(5), (2017); doi:10.1145/3117807 | |
dc.identifier.issn | 0360-0300 | |
dc.identifier.uri | http://hdl.handle.net/10651/45313 | |
dc.description.abstract | The task of quantification consists in providing an aggregate estimation (e.g. the class distribution in a classification problem) for unseen test sets, applying a model that is trained using a training set with a different data distribution. Several real-world applications demand this kind of methods that do not require predictions for individual examples and just focus on obtaining accurate estimates at an aggregate level. During the past few years, several quantification methods have been proposed from different perspectives and with different goals. This paper presents a unified review of the main approaches with the aim of serving as an introductory tutorial for newcomers in the field | |
dc.description.sponsorship | TIN2015-65069-C2-2-R, FEDER, Federación Española de Enfermedades Raras; MINECO, Ministerio de Economía y Competitividad; IIS-1447795, NSF, Norsk Sykepleierforbund | |
dc.format.extent | 37 p. (art. num. 74) | |
dc.language.iso | eng | |
dc.publisher | ACM | |
dc.relation.ispartof | ACM Computing Surveys, 50(5) | |
dc.rights | © 2017 ACM | |
dc.rights | CC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Class distribution estimation | |
dc.subject | Prevalence estimation | |
dc.subject | Quantification | |
dc.title | A review onquantification learning | eng |
dc.type | journal article | |
dc.identifier.doi | 10.1145/3117807 | |
dc.relation.projectID | MINECO-FEDER/TIN2015-65069-C2-2-R | |
dc.relation.publisherversion | http://dx.doi.org/10.1145/3117807 | |
dc.rights.accessRights | open access | |
dc.type.hasVersion | AM |
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