2026-06-17T23:53:40Zhttps://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

00925njm 22002777a 4500 dc Garrido Labrador, José Luis author Serrano Mamolar, Ana author Maudes Raedo, Jesús M. author Rodríguez Diez, Juan José author García Osorio, César author 2024-07 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. 1566-2535 http://hdl.handle.net/10259/8872 10.1016/j.inffus.2024.102310 Semi-supervised learning Ensemble learning Information fusion Semi-supervised ensemble classification Label scarcity Bibliographic review Research trends Experimental protocols Ensemble methods and semi-supervised learning for information fusion: A review and future research directions