# Comparing Bayesian Network Classifiers

arXiv: https://arxiv.org/abs/1301.6684 · HTML: https://mlsanity.com/paper/1301.6684/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2013.

## Built on by (most cited first, 5 of 5)

- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [Causality from Bottom to Top: A Survey](https://mlsanity.com/paper/2403.11219/) (2024)
- [An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques](https://mlsanity.com/paper/2412.09063/) (2024)
- [Towards the Next-generation Bayesian Network Classifiers](https://mlsanity.com/paper/2508.11145/) (2025)
- [Structure-Aware Robust Counterfactual Explanations via Conditional Gaussian Network Classifiers](https://mlsanity.com/paper/2602.08021/) (2026)

## Abstract (arXiv)

In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes and general BNs, where the latter two are learned using two variants of a conditional-independence (CI) based BN-learning algorithm. Experimental results show the obtained classifiers, learned using the CI based algorithms, are competitive with (or superior to) the best known classifiers, based on both Bayesian networks and other formalisms; and that the computational time for learning and using these classifiers is relatively small. Moreover, these results also suggest a way to learn yet more effective classifiers; we demonstrate empirically that this new algorithm does work as expected. Collectively, these results argue that BN classifiers deserve more attention in machine learning and data mining communities.