2018
DOI: 10.1371/journal.pone.0203546
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Abstract: Behaviors are important indicators for assessing the health and well-being of dairy cows. The aim of this study is to develop and validate an ensemble classifier for automatically measuring and distinguishing several behavior patterns of dairy cows from accelerometer data and location data. The ensemble classifier consists of two parts, our new Multi-BP-AdaBoost algorithm and a data fusion method based on D-S evidence theory. We identify seven behavior patterns: feeding, lying, standing, lying down, standing u… Show more

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Cited by 31 publications
(19 citation statements)
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References 26 publications
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“…AB Classifier proposed by Freund and Schapire [72] is a method that strategically combines a collection of ''weak'' classifiers to form a stronger classifier. Its advantage includes lower memory and computational requirements [73]. ET classifier implements a meta estimator that fits a number of randomized decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.…”
Section: E Ten Classifiersmentioning
confidence: 99%
“…AB Classifier proposed by Freund and Schapire [72] is a method that strategically combines a collection of ''weak'' classifiers to form a stronger classifier. Its advantage includes lower memory and computational requirements [73]. ET classifier implements a meta estimator that fits a number of randomized decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.…”
Section: E Ten Classifiersmentioning
confidence: 99%
“…Real-time location systems can measure these parameters automatically and provide data for early detection of behavior changes relevant to a cow's health and welfare. Furthermore, sensors that combine technologies (accelerometer and location) are promising tools because they have the potential to collect multiple parameters, thus improving algorithm performance to classify behaviors (Wang et al, 2018). The objectives of this study were (1) to determine the accuracy of the system in predicting cow location and the agreement between visual observations (VO) and observations of the RTLS for the total time spent by cows in relevant areas of the barn and (2) to compare the performance of 2 algorithms (Alg1 and Alg2) for cow location.…”
mentioning
confidence: 99%
“…Furthermore, in other studies, location data have typically been combined with accel- eration data in an attempt to increase the performance in behavior classification. Wang et al (2018) demonstrated that combining these 2 technologies improved the classification of feeding behavior by 20%. Furthermore, combining location with accelerometer data may improve the automated assessment of health and welfare (Chapa et al, 2020).…”
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confidence: 99%
“…Aiming at the problems of traditional dairy cow individual behavior detection methods, acceleration sensors and geomagnetic sensors were used to collect accelerated data and geomagnetic data during cow activities, then different classifiers based on the deeplearning model were used to monitor multiple behaviors. In recent years, acceleration sensors have been used for animal behavior identification due to their relevance and potential applications [24], [27], [29], [31]- [40].…”
Section: A Objectivementioning
confidence: 99%