Behavioural data

We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25-49 days old) were used to train and test the algorithm and, unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains the random forest model classified behaviours with very good accuracy (92%), specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%).本研究采用随机森林（random forest）算法处理肉鸡的加速度计（accelerometer）数据。研究采集了3个肉鸡品系在25至49日龄范围内的数据集，用于该算法的训练与测试；与现有同类研究不同，本研究进一步将训练完成的模型在一个未参与训练的肉鸡品系上开展了泛化性能测试。当在来自3个训练用肉鸡品系的未参与训练的肉鸡个体上进行测试时，随机森林模型的行为分类表现优异：准确率达92%、特异度为94%，灵敏度与精确率均为88%。而在该全新未参与训练的品系上进行测试时，模型的行为分类准确率达94%、灵敏度91%、特异度96%、精确率91%。