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Conservation machine learning: a case study of random forests
Conservation machine learning conserves models across runs, users, and experiments—and puts them to good use. We have previously shown the merit of this idea through a small-scale preliminary experiment, involving a single dataset source, 10 datasets, and a single so-called cultivation method—used t...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7878914/ https://www.ncbi.nlm.nih.gov/pubmed/33574563 http://dx.doi.org/10.1038/s41598-021-83247-4 |
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author | Sipper, Moshe Moore, Jason H. |
author_facet | Sipper, Moshe Moore, Jason H. |
author_sort | Sipper, Moshe |
collection | PubMed |
description | Conservation machine learning conserves models across runs, users, and experiments—and puts them to good use. We have previously shown the merit of this idea through a small-scale preliminary experiment, involving a single dataset source, 10 datasets, and a single so-called cultivation method—used to produce the final ensemble. In this paper, focusing on classification tasks, we perform extensive experimentation with conservation random forests, involving 5 cultivation methods (including a novel one introduced herein—lexigarden), 6 dataset sources, and 31 datasets. We show that significant improvement can be attained by making use of models we are already in possession of anyway, and envisage the possibility of repositories of models (not merely datasets, solutions, or code), which could be made available to everyone, thus having conservation live up to its name, furthering the cause of data and computational science. |
format | Online Article Text |
id | pubmed-7878914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78789142021-02-12 Conservation machine learning: a case study of random forests Sipper, Moshe Moore, Jason H. Sci Rep Article Conservation machine learning conserves models across runs, users, and experiments—and puts them to good use. We have previously shown the merit of this idea through a small-scale preliminary experiment, involving a single dataset source, 10 datasets, and a single so-called cultivation method—used to produce the final ensemble. In this paper, focusing on classification tasks, we perform extensive experimentation with conservation random forests, involving 5 cultivation methods (including a novel one introduced herein—lexigarden), 6 dataset sources, and 31 datasets. We show that significant improvement can be attained by making use of models we are already in possession of anyway, and envisage the possibility of repositories of models (not merely datasets, solutions, or code), which could be made available to everyone, thus having conservation live up to its name, furthering the cause of data and computational science. Nature Publishing Group UK 2021-02-11 /pmc/articles/PMC7878914/ /pubmed/33574563 http://dx.doi.org/10.1038/s41598-021-83247-4 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Sipper, Moshe Moore, Jason H. Conservation machine learning: a case study of random forests |
title | Conservation machine learning: a case study of random forests |
title_full | Conservation machine learning: a case study of random forests |
title_fullStr | Conservation machine learning: a case study of random forests |
title_full_unstemmed | Conservation machine learning: a case study of random forests |
title_short | Conservation machine learning: a case study of random forests |
title_sort | conservation machine learning: a case study of random forests |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7878914/ https://www.ncbi.nlm.nih.gov/pubmed/33574563 http://dx.doi.org/10.1038/s41598-021-83247-4 |
work_keys_str_mv | AT sippermoshe conservationmachinelearningacasestudyofrandomforests AT moorejasonh conservationmachinelearningacasestudyofrandomforests |