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Federated Ensemble Regression Using Classification

Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of multiple models. In this paper, we present an ens...

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Autores principales: Orhobor, Oghenejokpeme I., Soldatova, Larisa N., King, Ross D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556384/
http://dx.doi.org/10.1007/978-3-030-61527-7_22
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author Orhobor, Oghenejokpeme I.
Soldatova, Larisa N.
King, Ross D.
author_facet Orhobor, Oghenejokpeme I.
Soldatova, Larisa N.
King, Ross D.
author_sort Orhobor, Oghenejokpeme I.
collection PubMed
description Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of multiple models. In this paper, we present an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We apply the proposed algorithm to a problem in precision medicine where the goal is to predict drug perturbation effects on genes in cancer cell lines. The proposed approach significantly outperforms the base case.
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spelling pubmed-75563842020-10-15 Federated Ensemble Regression Using Classification Orhobor, Oghenejokpeme I. Soldatova, Larisa N. King, Ross D. Discovery Science Article Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of multiple models. In this paper, we present an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We apply the proposed algorithm to a problem in precision medicine where the goal is to predict drug perturbation effects on genes in cancer cell lines. The proposed approach significantly outperforms the base case. 2020-09-19 /pmc/articles/PMC7556384/ http://dx.doi.org/10.1007/978-3-030-61527-7_22 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
spellingShingle Article
Orhobor, Oghenejokpeme I.
Soldatova, Larisa N.
King, Ross D.
Federated Ensemble Regression Using Classification
title Federated Ensemble Regression Using Classification
title_full Federated Ensemble Regression Using Classification
title_fullStr Federated Ensemble Regression Using Classification
title_full_unstemmed Federated Ensemble Regression Using Classification
title_short Federated Ensemble Regression Using Classification
title_sort federated ensemble regression using classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556384/
http://dx.doi.org/10.1007/978-3-030-61527-7_22
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