Using the XGBoost algorithm to classify neck and leg activity sensor data using on-farm health recordings for locomotor-associated diseases

•Locomotor-associated diseases could be successfully classified (86% AUROC & 81% F-Measure).•Computational load could be reduced by approximately two-thirds using feature pre-selection.•Different feature types and window-lengths were considered.•XGBoost is a capable and easy to use method for cl...

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Veröffentlicht in:	Computers and electronics in agriculture 2020-06, Vol.173, p.105404, Article 105404
Hauptverfasser:	Gertz, M., Große-Butenuth, K., Junge, W., Maassen-Francke, B., Renner, C., Sparenberg, H., Krieter, J.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Automation Classification Computing time Motion sensors Segmentation Sensors
Online-Zugang:	Volltext
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