Applying a Machine Learning Workbench: Experience with Agricultural Databases
Stephen Garner, Sally Jo Cunningham, Geoffrey Holmes, Craig G. Nevill-Manning, Ian H. Witten · 1996
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. 1 INTRODUCTION The Waikato Environment for Knowledge Analysis (WEKA 1 ) is a New Zealand government-sponsored initiative to investigate the application of machine learning to economically important problems in the agricultural industries. The overall ...