Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques.
Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques.
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DOI：
10.3389/fvets.2020.00523
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发表时间：
2020
影响因子：
3.2
通讯作者：
Horback K
中科院分区：
文献类型：
--
作者：
McVey C;Hsieh F;Manriquez D;Pinedo P;Horback K
传感器技术使动物行为学家能够长时间连续监测大量动物的行为。这为在商业环境中研究牲畜行为创造了新的机会，但也带来了新的方法学挑战。来自大型异质群体的密集采样的行为数据可能包含一系列复杂的模式和随机结构，这些模式和结构可能难以使用传统的探索性数据分析技术来可视化。这项研究的目的是评估无监督机器学习工具在从这些数据集中恢复复杂行为模式方面的有效性，以更好地为后续的统计建模提供信息。本方法学案例研究是使用挤奶顺序记录或奶牛进入挤奶厅时的排列顺序进行的。数据收集了6个月的时间从一个封闭的组200个混合奇偶荷斯坦牛的有机乳品。在队列的前面和后面的奶牛证明更一致的进入位置比动物在队列的中心，一个系统的模式的异质性更清楚地可视化使用熵估计，规模和分布自由的替代方差鲁棒离群值。然后使用降维技术来可视化奶牛之间的关系。没有证据表明社会凝聚力被恢复，但扩散映射嵌入证明比PCA更善于揭示这些数据的潜在线性几何。中位客厅入口位置从前和后牧场子期高度相关（R = 0.91），这表明了令人惊讶的程度的时间平稳性。然而，数据力学可视化显示了组中心动物亚组之间的异质非平稳性和非平稳性水平的时间离群值。重复测量模型恢复不一致的证据之间的关系进入位置和奶牛属性。相互条件熵检验是一种基于置换的方法，用于评估对非独立性具有鲁棒性的双变量相关性，证实了与峰值产奶量的显著但非线性关联，但揭示了年龄效应可能受到健康状况的混淆。最后，使用线性模型和相互条件熵检验，将记录的行为与通过耳标加速度计记录的行为相关联。这两种方法都恢复了一致的证据表明，在队列的各个部分中，家庭笔行为存在差异。
Sensor technologies allow ethologists to continuously monitor the behaviors of large numbers of animals over extended periods of time. This creates new opportunities to study livestock behavior in commercial settings, but also new methodological challenges. Densely sampled behavioral data from large heterogeneous groups can contain a range of complex patterns and stochastic structures that may be difficult to visualize using conventional exploratory data analysis techniques. The goal of this research was to assess the efficacy of unsupervised machine learning tools in recovering complex behavioral patterns from such datasets to better inform subsequent statistical modeling. This methodological case study was carried out using records on milking order, or the sequence in which cows arrange themselves as they enter the milking parlor. Data was collected over a 6-month period from a closed group of 200 mixed-parity Holstein cattle on an organic dairy. Cows at the front and rear of the queue proved more consistent in their entry position than animals at the center of the queue, a systematic pattern of heterogeneity more clearly visualized using entropy estimates, a scale and distribution-free alternative to variance robust to outliers. Dimension reduction techniques were then used to visualize relationships between cows. No evidence of social cohesion was recovered, but Diffusion Map embeddings proved more adept than PCA at revealing the underlying linear geometry of this data. Median parlor entry positions from the pre- and post-pasture subperiods were highly correlated (R = 0.91), suggesting a surprising degree of temporal stationarity. Data Mechanics visualizations, however, revealed heterogeneous non-stationary among subgroups of animals in the center of the group and herd-level temporal outliers. A repeated measures model recovered inconsistent evidence of a relationships between entry position and cow attributes. Mutual conditional entropy tests, a permutation-based approach to assessing bivariate correlations robust to non-independence, confirmed a significant but non-linear association with peak milk yield, but revealed the age effect to be potentially confounded by health status. Finally, queueing records were related back to behaviors recorded via ear tag accelerometers using linear models and mutual conditional entropy tests. Both approaches recovered consistent evidence of differences in home pen behaviors across subsections of the queue.
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DOI：
10.1016/0304-3762(75)90020-6
发表时间：
1975-01-01
期刊：
Applied Animal Ethology
影响因子：
--
作者：
Gadbury, J. C.
通讯作者：
Gadbury, J. C.
影响因子：
2.5
作者：
BEILHARZ, R. G.;MYLREA, P. J.
通讯作者：
MYLREA, P. J.
影响因子：
3.5
作者：
Beggs, D. S.;Jongman, E. C.;Fisher, A. D.
通讯作者：
Fisher, A. D.
影响因子：
1.3
作者：
DICKSON, DP;BARR, GR;WIECKERT, DA
通讯作者：
WIECKERT, DA
影响因子：
3.7
作者：
Fushing H;Liu SY;Hsieh YC;McCowan B
通讯作者：
McCowan B