Improved estimation of bovine weight trajectories using Support Vector Machine Classification
Improved estimation of bovine weight trajectories using Support Vector Machine Classification
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DOI：
10.1016/j.compag.2014.10.001
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发表时间：
2015-01-01
影响因子：
8.3
通讯作者：
Bahamonde, Antonio
中科院分区：
文献类型：
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作者：
Alonso, Jaime;Villa, Alfonso;Bahamonde, Antonio
牲畜饲养者的收益通常与牲畜的体重密切相关。在本文中，我们提出了一种方法来预测每只动物的重量，只要我们知道过去的进化的牛群。我们的方法利用的几何关系的轨迹的重量沿着的时间。从一组动物的数据集合开始，我们学习了一系列适合整个数据集的并行函数，而不是为每个个体使用一个回归函数。通过这种方式，我们的方法使动物只有一个或几个重量有一个准确的估计，他们的未来进化。因此，我们学习了一个定义在权重和时间空间上的函数F，它以这样一种方式分离轨迹，即F在每个轨迹上都有恒定的值。关键点是F的指定可以按照排序约束来完成，与偏好函数或有序回归器相同。因此，可以从分类SVM（支持向量机）获得F。为了评估该方法，我们使用了不同品种和年龄的牛的真实的世界数据集的集合。我们将证明，当只有几个可用的权重并且我们需要中期或长期预测时，我们的方法优于每个动物的单独回归。(C)2014爱思唯尔有限公司版权所有。
The benefits of livestock breeders are usually closely related to the weight of their animals. In this paper we present a method to anticipate the weight of each animal provided we know the past evolution of the herd. Our approach exploits the geometrical relationships of the trajectories of weights along the time. Starting from a collection of data from a set of animals, we learn a family of parallel functions that fits the whole data set, instead of having one regression function for each individual. In this way, our method enables animals with only one or a few weights to have an accurate estimation of their future evolution. Thus, we learn a function F defined on the space of weights and time that separates the trajectories in such a way that F has constant values on each trajectory. The key point is that the specification of F can be done in terms of ordering constraints, in the same way as preference functions or ordinal regressors. Therefore, F can be obtained from a classification SVM (Support Vector Machines). To evaluate the method, we have used a collection of real world data sets of bovines of different breeds and ages. We will show that our method outperforms the separate regression of each animal when there are only a few weights available and we need medium or long term predictions. (C) 2014 Elsevier B.V. All rights reserved.