Ensemble approaches for regression: A survey

By João Mendes-Moreira, Carlos Soares, Alípio Mário Jorge, Jorge Freire de Sousa. CSUR, 2012

690 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e30a47911cfb98934dc640eb669b9351827bd37b

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In Artificial Intelligence

Citations in the corpus

A Survey on ensemble learning under the era of deep learning (2021, methodology): “As for regression problem, the outputs of the base learners can be averaged (Mendes-Moreira et al. 2012) to produce the final output of the ensemble learner.”
Ensemble deep learning: A review (2021, background): “There exist multiple surveys in the literature which mainly focus on the review of ensemble learning like learning of ensemble models in classification problems [16, 17, 18, 19], regression problems [20, 21] and clustering [22].”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “Here, we use the ambiguity decomposition in the field of ensemble learning (Mendes-Moreira et al. 2012) to explain why QBC is able to quickly reduce the generalization error of the committee metamodels.”
Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, methodology): “Ensemble learning refers to a class of machine learning methods that construct a set of base learners and combine them to create a strong learner [54].”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “There are surveys on ensemble classification methods [4]– [8] and ensemble regression methods [9], [10] in the literature.”
When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, background): “14This topic has been studied in a broad regime, called ensemble learning [119], for aggregating various learning models to boost predictions.”