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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1995 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1995 Working Papers
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      Feature selection via the discovery of simple classification rules

      Holmes, Geoffrey; Nevill-Manning, Craig G.
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      uow-cs-wp-1995-10.pdf
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      Holmes, 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.
      Permanent Research Commons link: https://hdl.handle.net/10289/1088
      Abstract
      It 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.
      Date
      1995-04
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      95/10
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 1995 Working Papers [32]
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