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罗曼羊品种终体重预测的数据挖掘和机器学习算法比较。

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed.

机构信息

Department of Animal Science, Faculty of Agriculture, Igdir University, Iğdır, Türkiye.

Department of Animal Science, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Türkiye.

出版信息

PLoS One. 2023 Aug 3;18(8):e0289348. doi: 10.1371/journal.pone.0289348. eCollection 2023.

DOI:10.1371/journal.pone.0289348
PMID:37535638
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10399827/
Abstract

The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. In conclusion, the usage of CART procedure may be worthy of reflection for identifying breed standards and choosing superior sheep for meat yield in France.

摘要

本研究旨在通过使用初生重、初生类型、性别、哺乳期体重、哺乳期体重年龄、断奶体重、断奶体重年龄和最终体重年龄来预测罗曼羊的最终体重(4 月龄体重以选择未来的繁殖者)。为此,使用分类和回归树(CART)、多元自适应回归样条(MARS)和支持向量机回归(SVR)算法对罗曼羊的 393 只(238 只雌性和 155 只雄性动物)进行训练(80%)和测试(20%)集。不同的数据挖掘和机器学习算法用于预测最终体重。不同的人工智能算法用于预测 393 只罗曼羊的最终体重。通过 CART 模型获得了最佳预测模型,无论是在训练集还是测试集中。构建的 CART 模型表明,性别、哺乳期体重、断奶体重、断奶体重年龄和最终体重年龄可用作间接选择措施,以获得罗曼羊最终体重较高的羊群。如果从遗传上建立起来,那么选择最终体重年龄超过 142 天、断奶体重超过 28 公斤的雌性罗曼羊,可以在最终体重方面进行遗传改良。总之,CART 程序的使用可能值得在法国考虑,以确定品种标准并选择用于提高产肉量的优秀绵羊。

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