使用LightGBM、XGBoost和支持向量机算法通过体尺测量预测肉用杂交雌性兔的活重
Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms.
作者信息
Önder Hasan, Tirink Cem, Yakubets Taras, Getya Andriy, Matvieiev Mykhalio, Kononenko Ruslan, Şen Uğur, Özkan Çağri Özgür, Tolun Tolga, Kaya Fahrettin
机构信息
Faculty of Agriculture, Department of Animal Science, Ondokuz Mayis University, Samsun, Türkiye.
Faculty of Agriculture, Department of Animal Science, Igdir University, Iğdır, Türkiye.
出版信息
Vet Med Sci. 2025 Jan;11(1):e70149. doi: 10.1002/vms3.70149.
Prediction of body weight (BW) using biometric measurements is an important tool especially for animal welfare and automatic phenotyping tools that needs mathematical models. In this study, it was aimed to predict the BW using body length (BL), chest girth (CG) and width of the waist (WW) for rabbits of the maternal form of Hyla NG. The standard rabbit-raising practices were applied for the animals. A highly efficient gradient-boosting decision tree (LightGBM), eXtreme gradient-boosting (XGBoost) and support vector machine (SVM) algorithms were evaluated and compared to the prediction of BW. The coefficient of determination, root mean square error and mean absolute error values were used as comparison criteria. The results showed that LightGBM, XGBoost and SVM algorithms were well fit for the BW using the biometric measures with over 95% accuracy for both train and test sets. The BL was determined as the most explanatory variable on body weight.
利用生物特征测量预测体重（BW）是一项重要工具，尤其对于需要数学模型的动物福利和自动表型分析工具而言。在本研究中，旨在利用体长（BL）、胸围（CG）和腰围（WW）对Hyla NG母系品种的兔子进行体重预测。对这些动物采用了标准的养兔方法。评估并比较了高效梯度提升决策树（LightGBM）、极端梯度提升（XGBoost）和支持向量机（SVM）算法对体重的预测。决定系数、均方根误差和平均绝对误差值用作比较标准。结果表明，LightGBM、XGBoost和SVM算法在使用生物特征测量预测体重方面拟合良好，训练集和测试集的准确率均超过95%。体长被确定为对体重最具解释力的变量。
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