A Bayesian Network Classifier that Combines a Finite Mixture Model and a Naive Bayes Model
This work addresses classification accuracy and calibration for machine learning practitioners, but it is incremental as it builds on existing models.
The paper tackles the problem of improving classification performance by combining naive Bayes and finite mixture models to relax their strong assumptions, resulting in a new Bayesian network classifier that shows improved accuracy and probability calibration on real datasets.
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.