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.

中文翻译：

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信息融合的集成方法和半监督学习：回顾和未来研究方向

本文研究了过去十年信息融合方法和半监督学习 (SSL) 交叉领域的进展，以应对与有限标记数据相关的挑战。为此，我们对 2013 年以来发表的论文进行了书目回顾，其中集成方法与新的机器学习算法相结合。总共有 128 个使用 SSL 算法进行集成构建的新提案被识别和分类。所有方法均按方法、集成类型和基分类器进行分类。还评估了实验方案、预处理、数据集使用、未标记比率和统计测试，强调了主要趋势和特定研究的一些缺点。从这篇文献综述中可以明显看出，自训练和协同训练等基础算法正在影响当前的发展，并且创新的集成技术正在不断出现。此外，评论中还确定了有价值的指南，用于改进本质上半监督和无监督预处理方法的研究，特别是回归任务。