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      • 2003 Working Papers
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      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2003 Working Papers
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      Locally weighted naive Bayes

      Frank, Eibe; Hall, Mark A.; Pfahringer, Bernhard
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      Frank, E., Hall, M. & Pfahringer, B. (2003). Locally weighted naive Bayes. (Working paper 04/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1008
      Abstract
      Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes' primary weakness—attribute independence—and improve the performance of the algorithm. This paper presents a locally weighted version of naive Bayes that relaxes the independence assumption by learning local models at prediction time. Experimental results show that locally weighted naive Bayes rarely degrades accuracy compared to standard naive Bayes and, in many cases, improves accuracy dramatically. The main advantage of this method compared to other techniques for enhancing naive Bayes is its conceptual and computational simplicity.
      Date
      2003-04
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      04/03
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 2003 Working Papers [8]
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