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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1998 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1998 Working Papers
      • View Item
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      Experiences with a weighted decision tree learner

      Cleary, John G.; Trigg, Leonard E.
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      Cleary, J. G. & Trigg, L. E. (1998). Experiences with a weighted decision tree learner. (Working paper 98/10). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1055
      Abstract
      Machine learning algorithms for inferring decision trees typically choose a single “best” tree to describe the training data. Recent research has shown that classification performance can be significantly improved by voting predictions of multiple, independently produced decision trees. This paper describes an algorithm, OB1, that makes a weighted sum over many possible models. We describe one instance of OB1, that includes all possible decision trees as well as naïve Bayesian models. OB1 is compared with a number of other decision tree and instance based learning algorithms on some of the data sets from the UCI repository. Both an information gain and an accuracy measure are used for the comparison. On the information gain measure OB1 performs significantly better than all the other algorithms. On the accuracy measure it is significantly better than all the algorithms except naïve Bayes which performs comparably to OB1.
      Date
      1998-05
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      98/10
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 1998 Working Papers [18]
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