Show simple item record  

dc.contributor.authorMitchell, R. Scott
dc.contributor.authorSherlock, Robert A.
dc.contributor.authorSmith, Lloyd A.
dc.date.accessioned2008-10-21T00:38:51Z
dc.date.available2008-10-21T00:38:51Z
dc.date.issued1995-08
dc.identifier.citationMitchell, R. S., Sherlock, R. A. & Smith, L. A. (1995) An investigation into the use of machine learning for determining oestrus in cows. (Working paper 95/23). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1100
dc.description.abstractA preliminary investigation of the application of two well-known machine learning schemes—C4.5 and FOIL—to detection of oestrus in dairy cows has been made. This is a problem of practical economic significance as each missed opportunity for artificial insemination results in 21 days lost milk production. Classifications were made on normalised deviations of milk volume production and milking order time series data. The best learning scheme was C4.5 which was able to detect 69% of oestrus events, albeit with an unacceptably high rate of "false positives" (74%). Several directions for further work and improvements are identified.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato, Department of Computer Scienceen_US
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectmachine learningen_US
dc.subjectoestrus detectionen_US
dc.subjectdairy cowen_US
dc.titleAn investigation into the use of machine learning for determining oestrus in cowsen_US
dc.typeWorking Paperen_US
uow.relation.series95/23


Files in this item

This item appears in the following Collection(s)

Show simple item record