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      • 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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      Inducing cost-sensitive trees via instance-weighting

      Ting, Kai Ming
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      Ting, K.M. (1997). Inducing cost-sensitive trees via instance-weighting. (Working paper 97/22). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1118
      Abstract
      We introduce an instance-weighting method to induce cost-sensitive trees in this paper. It is a generalization of the standard tree induction process where only the initial instance weights determine the type of tree (i.e., minimum error trees or minimum cost trees) to be induced. We demonstrate that it can be easily adopted to an existing tree learning algorithm.

      Previous research gave insufficient evidence to support the fact that the greedy divide-and-conquer algorithm can effectively induce a truly cost-sensitive tree directly from the training data. We provide this empirical evidence in this paper. The algorithm employing the instance-weighting method is found to be comparable to or better than both C4.5 and C5 in terms of total misclassification costs, tree size and the number of high cost errors. The instance-weighting method is also simpler and more effective in implementation than a method based on altered priors.
      Date
      1997-09
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      97/22
      Publisher
      Computer Science, University of Waikato
      Collections
      • 1997 Working Papers [31]
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