A comparative analysis of discretization methods for medical datamining with Naïve Bayesian classifier Conference

Abraham, R, Simha, JB, Iyengar, SS. (2006). A comparative analysis of discretization methods for medical datamining with Naïve Bayesian classifier . 235-236. 10.1109/ICIT.2006.5

cited authors

  • Abraham, R; Simha, JB; Iyengar, SS

authors

abstract

  • Naïve Bayes classifier has gained wide popularity as a probability-based classification method despite its assumption that attributes are conditionally mutually independent given the class label. This paper makes a study into discretization techniques to improve the classification accuracy of Naïve Bayes with respect to medical datasets. Our experimental results suggest that on an average, with Minimum Description Length (MDL) discretization the Naïve Bayes Classifier seems to be the best performer compared to popular variants of Naïve Bayes as well as some popular non-Naïve Bayes statistical classifiers. © 2006 IEEE.

publication date

  • January 1, 2006

Digital Object Identifier (DOI)

start page

  • 235

end page

  • 236