Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models-Reference-Cited by-同舟云学术

Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models

Published:2024-06 Issue:6 Volume:107 Page:3959-3972
ISSN:0022-0302
Container-title:Journal of Dairy Science
language:en
Short-container-title:Journal of Dairy Science

Author:

Satoła A.^ORCID,Satoła K.^ORCID

Funder

Ministry of Science and Higher Education of the Republic of Poland

Publisher

American Dairy Science Association

Reference33 articles.

1. Comparison of methods for predicting cow composite somatic cell counts;Anglart;J. Dairy Sci.,2020

2. The use of multilayer perceptron artificial neural networks to detect dairy cows at risk of ketosis;Bauer;Animals (Basel),2022

3. Predicting dairy cattle heat stress using machine learning techniques;Becker;J. Dairy Sci.,2021

4. Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows;Bobbo;Sci. Rep.,2021

5. Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes;Bobbo;J. Dairy Sci.,2023

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