On Construction of Hybrid Logistic Regression-Naïve Bayes Model for Classification
; Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:523-534, 2016.
In recent years, several authors have described a hybrid discriminative-generative model for classification. In this paper we examine construction of such hybrid models from data where we use logistic regression (LR) as a discriminative component, and naïve Bayes (NB) as a generative component. First, we estimate a Markov blanket of the class variable to reduce the set of features. Next, we use a heuristic to partition the set of features in the Markov blanket into those that are assigned to the LR part, and those that are assigned to the NB part of the hybrid model. The heuristic is based on reducing the conditional dependence of the features in NB part of the hybrid model given the class variable. We implement our method on 21 different classification datasets, and we compare the prediction accuracy of hybrid models with those of pure LR and pure NB models.