2026-06-16T17:24:35Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/88722024-04-02T00:05:11Zcom_10259_5377com_10259_5086com_10259_2604com_10259_4219col_10259_5378col_10259_4220

Garrido Labrador, José Luis 745 600 0000-0002-3441-4223 Serrano Mamolar, Ana 820 600 0000-0002-0027-7128 Maudes Raedo, Jesús M. 352 600 0000-0001-8808-412X Rodríguez Diez, Juan José 477 600 0000-0002-3291-2739 García Osorio, César 212 600 0000-0002-1206-1084 2024-04-01T10:59:56Z 2024-04-01T10:59:56Z 2024-07 1566-2535 http://hdl.handle.net/10259/8872 10.1016/j.inffus.2024.102310 Advances over the past decade at the intersection of information fusion methods and Semi-Supervised Learning (SSL) are investigated in this paper that grapple with challenges related to limited labelled data. To do so, a bibliographic review of papers published since 2013 is presented, in which ensemble methods are combined with new machine learning algorithms. A total of 128 new proposals using SSL algorithms for ensemble construction are identified and classified. All the methods are categorised by approach, ensemble type, and base classifier. Experimental protocols, pre-processing, dataset usage, unlabelled ratios, and statistical tests are also assessed, underlining the major trends, and some shortcomings of particular studies. It is evident from this literature review that foundational algorithms such as self-training and co-training are influencing current developments, and that innovative ensemble techniques are continuing to emerge. Additionally, valuable guidelines are identified in the review for improving research into intrinsically semi-supervised and unsupervised pre-processing methods, especially for regression tasks. This work was supported through the Junta de Castilla y León (JCyL) (regional goverment) under project BU055P20 (JCyL/FEDER, UE), the Spanish Ministry of Science and Innovation under project PID2020-119894GB-I00 co-financed through European Union FEDER funds, and project TED2021-129485B-C43 funded by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR. J.L. Garrido-Labrador is supported through Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant EDU/875/2021 (Spain). application/pdf eng Elsevier Information Fusion. 2024, V. 107, 102310 https://doi.org/10.1016/j.inffus.2024.102310 info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-129485B-C43/ES/Sistemas dinámicos inteligentes centrados en el usuario para la Prevención de Riesgos Laborales/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Semi-supervised learning Ensemble learning Information fusion Semi-supervised ensemble classification Label scarcity Bibliographic review Research trends Experimental protocols Informática Computer science Ensemble methods and semi-supervised learning for information fusion: A review and future research directions info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Information Fusion 107 102310 THUMBNAIL Garrido-if_2024.pdf.jpg Garrido-if_2024.pdf.jpg IM Thumbnail image/jpeg 4829 https://riubu.ubu.es/bitstream/10259/8872/4/Garrido-if_2024.pdf.jpg f9e64bfddc5e36dea36411a8d333e6ec MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/8872/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/8872/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Garrido-if_2024.pdf Garrido-if_2024.pdf application/pdf 1716280 https://riubu.ubu.es/bitstream/10259/8872/1/Garrido-if_2024.pdf 940c662fbc38f4059b6d8d7270bc9d54 MD5 1 10259/8872 oai:riubu.ubu.es:10259/8872 2024-04-02 02:05:11.027 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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