A Bayesian Network Classifier that Combines a Finite Mixture Model and a Naive Bayes Model

TL;DR

This paper introduces a new Bayesian network classifier that combines naive Bayes and finite mixture models to improve classification accuracy and probability calibration, demonstrated through experiments on real datasets.

Contribution

A novel Bayesian classifier that integrates finite mixture models with naive Bayes to relax assumptions and enhance performance.

Findings

Improved classification accuracy over traditional models

Better calibration of estimated probabilities

Effective on real-world datasets

Abstract

In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification performance, both in terms of accuracy and in terms of calibration of the estimated probabilities. The proposed classifier is obtained by superimposing a finite mixture model on the set of feature variables of a naive Bayes model. We present experimental results that compare the predictive performance on real datasets of the new classifier with the predictive performance of the NB classifier and the FM classifier.