Naive Bayes Classifier with Mixtures of Polynomials
J. Luengo, Rafael Rumí · 2015
We present in this paper a methodology for including continuous features in the Naive Bayes classifier by estimating the density function of the continuous variables through the Mixtures of Polynomials model. Three new issues are considered for this model: i) a classification oriented parameter estimation procedure ii) a feature selection procedure and iii) the definition of new kind of variable, to deal with those variables that are in theory continuous, but their behavior makes the estimation difficult. These methods are tested with respect to classical discrete and Gaussian Naive Bayes, as well as classification trees.