对数据挖掘和机器学习算法的比较,用于预测罗姆羊品种的最终体重
Cem Tırınk1, Hasan Önder2, Dominique Francois3
1Department of Animal Science, Faculty of Agriculture, Igdir University, Iğdır, Türkiye.
PloS one
|August 3, 2023
概括
归类和回归树 (CART) 模型准确地预测了罗姆羊的最终体重. 关键预测因素包括性别,断奶体重和年龄,使得肉类产量的遗传改善成为可能.
科学领域:
- 动物科学动物科学
- 机器学习 机器学习
- 遗传学 是一个遗传学.
背景情况:
- 准确预测最终体重对于选择优质罗姆羊进行繁殖和肉类产量至关重要.
- 传统的选择方法可以通过利用数据挖掘和人工智能算法来增强.
研究的目的:
- 用各种机器学习算法预测罗姆羊的最终体重.
- 为间接选择策略确定影响最终体重的关键因素.
主要方法:
- 采用了分类和回归树 (CART),多变量自适应回归支柱 (MARS) 和支持向量机回归 (SVR) 算法.
- 在393只罗姆羊的数据集上训练和测试模型,利用诸如出生体重,性别和断奶数据等特征.
- 评估模型在培训 (80%) 和测试 (20%) 集中的性能.
主要成果:
- 在训练和测试数据集中,CART模型展示了最终体重的最佳预测性能.
- 确定了性别,哺乳体重,断奶体重,断奶年龄和最终体重年龄作为重要预测因素.
- 拟议的基因改进标准:142天以上的雌性罗姆羊,断奶体重超过28公斤.
结论:
- 卡特算法是预测罗姆羊最终体重和促进间接选择的宝贵工具.
- 这些发现有助于制定品种标准,并识别法国用于改良肉类生产的优质动物.
- 整合机器学习模型为绵羊养殖中遗传改进提供了一个有希望的方法.
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