Applying a Machine Learning Workbench: Experience with Agricultural Databases



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Applying a Machine Learning Workbench:

Experience with Agricultural Databases

Stephen R. Garner, Sally Jo Cunningham, Geoffrey Holmes,

Craig G. Nevill-Manning and Ian H. Witten

Computer Science Department,

University of Waikato,

Hamilton, New Zealand.

{srg1,sallyjo,geoff,cgn,ihw}@cs.waikato.ac.nz

Abstract


This paper reviews our experience with the application of machine learning techniques to agricultural databases. We have designed and implemented a machine learning workbench, weka, which permits rapid experimentation on a given dataset using a variety of machine learning schemes, and has several facilities for interactive investigation of the data: preprocessing attributes, evaluating and comparing the results of different schemes, and designing comparative experiments to be run off-line. We discuss the partnership between agricultural scientist and machine learning researcher that our experience has shown to be vital to success. We review in some detail a particular agricultural application concerned with the culling of dairy herds.



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