使用多变量自适应回归脊柱 (MARS) 和分类和回归树 (CART) 数据挖掘算法来预测Tswana羊的活体体重
Monosi Andries Bolowe1, Lubabalo Bila2, Ketshephaone Thutwa1
1Department of Animal Sciences, Botswana University of Agriculture and Natural Resources, Private Bag, Gaborone 0027, Botswana.
Biology
|November 27, 2025
概括
心脏周长是Tswana羊活体体重的强有力的预测指标. 与分类和回归树 (CART) 算法相比,多变量自适应回归脊柱 (MARS) 算法证明了身体体重的优异预测性能.
科学领域:
- 动物科学动物科学
- 遗传学 是一个遗传学.
- 数据挖掘 数据挖掘
背景情况:
- 准确预测活体体重 (BW) 对牲畜的遗传改进计划至关重要.
- 生物识别特征为估计BW提供了一种非侵入性方法.
- 作为一个重要的本土品种的Tswana羊,需要有效的BW评估方法.
研究的目的:
- 为了确定BW和Tswana羊的生物特征之间的关联.
- 评估多变量自适应回归支柱 (MARS) 和分类和回归树 (CART) 算法在预测BW方面的有效性.
- 在这个品种中确定BW预测的最佳算法.
主要方法:
- 收集了392只Tswana羊 (3-4岁) 的BW和16个生物识别特征.
- 使用Pearson的相关系数来评估BW-生物识别特征关系.
- 利用MARS和CART算法来建模和预测BW,并使用适合性标准比较其性能.
主要成果:
- 活体体重显示出与心脏周长 (HG) 的高度显著的正相关性 (r=0.99).
- HG被确定为BW的唯一显著预测因素.
- 与CART算法相比,MARS算法对BW表现出更高的预测准确度.
结论:
- 心脏周长是一个可靠和有效的生物识别指标,用于预测Tswana羊的BW.
- 建议使用MARS算法来开发Tswana羊的BW预测模型.
- 这些发现为基因选择和改进计划提供了有价值的工具,旨在增强Tswana羊的BW.
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