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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1997 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1997 Working Papers
      • View Item
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      Discovering inter-attribute relationships

      Holmes, Geoffrey
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      uow-cs-wp-1997-13.pdf
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      Holmes, G. (1997). Discovering inter-attribute relationships. (Working paper 97/13). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1076
      Abstract
      It is important to discover relationships between attributes being used to predict a class attribute in supervised learning situations for two reasons. First, any such relationship will be potentially interesting to the provider of a dataset in its own right. Second, it would simplify a learning algorithm’s search space, and the related irrelevant feature and subset selection problem, if the relationships were removed from datasets ahead of learning. An algorithm to discover such relationships is presented in this paper. The algorithm is described and a surprising number of inter-attribute relationships are discovered in datasets from the University of California at Irvine (UCI) repository.
      Date
      1997-04
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      97/13
      Collections
      • 1997 Working Papers [31]
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