The comparative study of different Bayesian classifier models

Abstract

The Bayesian classifier model is a class of probability classifier based on the Bayesian theory. Compared with more sophisticated classification algorithms, such as decision tree and neural network, Bayesian classifier can offer very good classification accuracy in many practical applications. In this article, we perform a methodologically sound comparison of the seven methods, which shows large mutual differences of each of the methods and no single method being universally better. The comparisons that are carried out in this paper include time complexity and classification accuracy of these seven algorithms.