2024
Garrido-Labrador, José Luis; Serrano-Mamolar, Ana; Maudes-Raedo, Jesús; Rodríguez, Juan José; García-Osorio, César
Ensemble methods and semi-supervised learning for information fusion: A review and future research directions Journal Article
In: Information Fusion, vol. 107, 2024.
Abstract | Links | BibTeX | Tags: Bibliographic review, Ensemble learning, Experimental protocol, Information fusion, Label scarsity, Research trends, Semi-supervised ensemble classification, Semi-supervised learning
@article{garrido2024ensemble,
title = {Ensemble methods and semi-supervised learning for information fusion: A review and future research directions},
author = {José Luis Garrido-Labrador and Ana Serrano-Mamolar and Jesús Maudes-Raedo and Juan José Rodríguez and César García-Osorio},
url = {https://doi.org/10.1016/j.inffus.2024.102310},
doi = {10.1016/j.inffus.2024.102310},
year = {2024},
date = {2024-02-02},
urldate = {2024-02-02},
journal = {Information Fusion},
volume = {107},
abstract = {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 …
},
keywords = {Bibliographic review, Ensemble learning, Experimental protocol, Information fusion, Label scarsity, Research trends, Semi-supervised ensemble classification, Semi-supervised learning},
pubstate = {published},
tppubtype = {article}
}
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 …