Holmes, GeoffreyNevill-Manning, Craig G.2008-10-202008-10-201995-04Holmes, G. & Nevill-Manning, C. G. (1995). Feature selection via the discovery of simple classification rules. (Working paper 95/10). Hamilton, New Zealand: University of Waikato, Department of Computer Science.1170-487Xhttps://hdl.handle.net/10289/1088It has been our experience that in order to obtain useful results using supervised learning of real-world datasets it is necessary to perform feature subset selection and to perform many experiments using computed aggregates from the most relevant features. It is, therefore, important to look for selection algorithms that work quickly and accurately so that these experiments can be performed in a reasonable length of time, preferably interactively. This paper suggests a method to achieve this using a very simple algorithm that gives good performance across different supervised learning schemes and when compared to one of the most common methods for feature subset selection.application/pdfencomputer sciencefeature subset selectionsupervised learning1Rfilter modelwrapper modelMachine learningWorking Paper