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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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      Subset selection using rough numeric dependency

      Smith, Tony C.; Holmes, Geoffrey
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      uow-cs-wp-1995-12.pdf
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      Smith, T. C. & Holmes, G. (1995). Subset selection using rough numeric dependency. (Working paper 95/12). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1090
      Abstract
      In this paper we describe a novel method for performing feature subset selection for supervised learning tasks based on a refined notion of feature relevance. We define relevance as others see it and outline our refinement of this concept. We then describe how we use this new definition in an algorithm to perform subset selection, and finally, we show some preliminary results of using this approach with two quite different supervised learning schemes.
      Date
      1995-04
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      95/12
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 1995 Working Papers [32]
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