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      • 2006 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2006 Working Papers
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      A Decision tree-based attribute weighting filter for naive Bayes

      Hall, Mark A.
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      Hall, M. (2006). A Decision tree-based attribute weighting filter for naive Bayes. (Working paper series. University of Waikato, Department of Computer Science. No. 05/2006). Hamilton, New Zealand: University of Waikato.
      Permanent Research Commons link: https://hdl.handle.net/10289/101
      Abstract
      The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness--the assumption that attributes are independent given the class. All of them improve the performance of naïve Bayes at the expense (to a greater or lesser degree) of execution time and/or simplicity of the final model. In this paper we present a simple filter method for setting attribute weights for use with naive Bayes. Experimental results show that naive Bayes with attribute weights rarely degrades the quality of the model compared to standard naive Bayes and, in many cases, improves it dramatically. The main advantages of this method compared to other approaches for improving naive Bayes is its

      run-time complexity and the fact that it maintains the simplicity of the final model.
      Date
      2006-05-01
      Type
      Working Paper
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      • 2006 Working Papers [10]
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