RT info:eu-repo/semantics/article
T1 Ensemble methods and semi-supervised learning for information fusion: A review and future research directions
A1 Garrido Labrador, José Luis
A1 Serrano Mamolar, Ana
A1 Maudes Raedo, Jesús M.
A1 Rodríguez Diez, Juan José
A1 García Osorio, César
K1 Semi-supervised learning
K1 Ensemble learning
K1 Information fusion
K1 Semi-supervised ensemble classification
K1 Label scarcity
K1 Bibliographic review
K1 Research trends
K1 Experimental protocols
K1 Informática
K1 Computer science
AB 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.
PB Elsevier
SN 1566-2535
YR 2024
FD 2024-07
LK http://hdl.handle.net/10259/8872
UL http://hdl.handle.net/10259/8872
LA eng
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 16-may-2024