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      Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data

      Lynam, Adam David
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      Lynam, A. D. (2009). Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2777
      Permanent Research Commons link: https://hdl.handle.net/10289/2777
      Abstract
      The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of heat. Many areas of the dairy industry can be automated, however the detection of oestrus is an area that still requires human experts.

      This thesis investigates the application of Machine Learning classification techniques, on dairy cow milking data provided by the Livestock Improvement Corporation, to predict oestrus. The usefulness of various ensemble learning algorithms such as Bagging and Boosting are explored as well as specific skewed data techniques.

      An empirical study into the effectiveness of classifiers designed to target skewed data is included as a significant part of the investigation. Roughly Balanced Bagging and the novel Under Bagging classifiers are explored in considerable detail and found to perform quite favourably over the SMOTE technique for the datasets selected. This study uses non-dairy, commonplace, Machine Learning datasets; many of which are found in the UCI Machine Learning Repository.
      Date
      2009
      Type
      Thesis
      Degree Name
      Master of Science (MSc)
      Publisher
      The University of Waikato
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      All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
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      • Masters Degree Theses [2470]
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