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      • Computing and Mathematical Sciences
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      Naïve Bayes for text classification with unbalanced classes

      Frank, Eibe; Bouckaert, Remco R.
      DOI
       10.1007/11871637_49
      Link
       www.springerlink.com
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      Citation
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      Frank, E. & Bouckaert, R. R. (2006). Naïve Bayes for text classification with unbalanced classes. In J. Fürnkranz, T. Scheffer, & M. Spiliopoulou(Eds.), Proceedings of 10th European Conference on Principles and Practice of Knowledge Discovery in Databases Berlin, Germany, September 18-22, 2006(pp. 503-510). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/1442
      Abstract
      Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1,2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.
      Date
      2006
      Type
      Conference Contribution
      Publisher
      Springer, Berlin
      Collections
      • Computing and Mathematical Sciences Papers [1455]
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