Behavior classification of goats using 9-axis multi sensors: The effect of imbalanced datasets on classification performance
Behavior classification of goats using 9-axis multi sensors: The effect of imbalanced datasets on classification performance
复制标题
使用 9 轴多传感器对山羊进行行为分类：不平衡数据集对分类性能的影响
DOI：
10.1016/j.compag.2019.105027
复制
发表时间：
2019
影响因子：
8.3
通讯作者：
H.
中科院分区：
文献类型：
--
作者：
Sakai;K.;Oishi;K.;Miwa;M.;Kumagai;H.;and Hirooka;H.
小型电子仪器的最新发展使得通过同时测量各种生物记录数据(如加速度、磁性和角速度)来对动物行为进行分类成为可能。随着技术的进步，基于加速计、磁力计和陀螺仪的测量相结合的反刍动物行为分类的研究受到了关注。然而，尽管类不平衡问题最近已经成为机器学习分类中的一个严重挑战，但很少有针对家畜动物的行为分类研究集中在均衡每种行为的流行率来改善数据不平衡对分类性能造成的问题上。本研究的目的是使用背置的9轴多传感器(三轴加速度计、三轴陀螺仪和三轴磁力计)和机器学习算法对山羊的行为进行分类，并通过均衡每种行为的流行率来评估预测分数的变化。用多传感器记录了三只山羊在实验牧场上放牧约12 h的行为。在整个实验期间，用延时相机每隔1秒记录一次行为。三种行为被归类：躺着、站着和吃草。从原始传感器数据中提取100多个不同的变量，并将这些变量输入到两种有监督的机器学习算法：K最近邻(KNN)和决策树(DT)中进行分类。此外，由于与放牧相比，站立的流行率较低，因此通过欠采样使分类模型的训练数据集中每种行为的观察数量相等。正如预期的那样，结果表明，使用来自三个传感器的所有变量的两种算法的总体精度都高于仅使用来自加速度数据的变量的算法。此外，使用加速度和磁场数据变量的算法都可以像使用所有传感器数据变量的算法一样准确地对行为进行分类。平衡每种行为的流行程度，平卧和放牧分类的F1得分下降，而按DT分类的立地分类的F1得分略有上升。总而言之，我们的结果表明，除了三轴加速外，三轴磁性对于分类反刍动物的卧卧、站立和放牧活动是有用的，并且均衡每种行为的数据数量对于正确评估行为分类的预测准确性是重要的，特别是对于低流行率的行为。
Recent developments of small electronic instruments have enabled the classification of animal behavior using simultaneous measurements of various bio-logging data such as acceleration, magnetism, and angular velocity. Following technological progress, studies on the behavioral classification of ruminants combining measurements based on accelerometers, magnetometers, and gyroscopes have received attention. However, while the issue of class imbalance has recently become a serious challenge in classification by machine learning, few behavioral classification studies on livestock animals have focused on the effect of equalizing the prevalence of each behavior to improve the problem caused by the imbalance of data on classification performance. The aims of this study were to classify the behaviors of goats using a back-mounted 9-axis multi sensor (a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer) with machine learning algorithms, and to evaluate changes in the predictive scores by equalizing the prevalence of each behavior. The behaviors of three goats grazing on an experimental pasture were logged for approximately 12 h with the multi sensors. The behaviors were recorded at 1-second intervals with time-lapse cameras throughout the experimental period. Three behaviors were classified: lying, standing, and grazing. Over 100 different variables were extracted from the raw sensor data, and classification was executed by inputting the variables into two supervised machine learning algorithms: K-nearest neighbors (KNN) and decision tree (DT). Moreover, because the prevalence of standing was low compared to that of grazing, the number of observations of each behavior in the training datasets for classification models was equalized by undersampling. As expected, the results indicated that the overall accuracies of both algorithms using all variables derived from the three sensors were higher than those using only variables from the acceleration data. Furthermore, both the algorithms using the variables from the acceleration and magnetism data could classify the behaviors as accurate as the algorithms using variables from all sensor data. Balancing the prevalence of each behavior resulted in a decrease in the F1 scores of the lying and grazing classifications but a slight increase in those of the standing classification by DT. In conclusion, our results suggest that, in addition to tri-axial acceleration, tri-axial magnetism is useful for classifying lying, standing, and grazing activities of ruminants and that equalizing the number of data for each behavior is important to correctly assess the predictive accuracy of behavioral classifications, particularly for the behavior with low prevalence.