Bayesian Network Classifiers

@article{Friedman2004BayesianNC,
  title={Bayesian Network Classifiers},
  author={Nir Friedman and Dan Geiger and Mois{\'e}s Goldszmidt},
  journal={Machine Learning},
  year={2004},
  volume={29},
  pages={131-163}
}
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored… 
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References

SHOWING 1-10 OF 61 REFERENCES
Building Classifiers Using Bayesian Networks
TLDR
Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness which are characteristic of naive Baye.
Efficient Learning of Selective Bayesian Network Classifiers
TLDR
Experimental results show the resulting classifiers are competitive with (or superior to) the best classifiers, based on both Bayesian networks and other formalisms, and that the computational time for learning and using these classifiers is relatively small.
Induction of Selective Bayesian Classifiers
A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers
Searching for Dependencies in Bayesian Classifiers
TLDR
It is shown that the backward sequential elimination and joining algorithm provides the most improvement over the naive Bayesian classifier and that the violations of the independence assumption that affect the accuracy of the classifier can be detected from training data.
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TLDR
A methodology for assessing informative priors needed for learning Bayesian networks from a combination of prior knowledge and statistical data is developed and how to compute the relative posterior probabilities of network structures given data is shown.
An Analysis of Bayesian Classifiers
TLDR
An average-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks, and explores the behavioral implications of the analysis by presenting predicted learning curves for artificial domains.
Estimating Continuous Distributions in Bayesian Classifiers
TLDR
This paper abandon the normality assumption and instead use statistical methods for nonparametric density estimation for kernel estimation, which suggests that kernel estimation is a useful tool for learning Bayesian models.
Learning Bayesian Networks with Local Structure
TLDR
A novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks and indicates that learning curves characterizing the procedure that exploits the local structure converge faster than these of the standard procedure.
LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE
TLDR
A new approach for learning Bayesian belief networks from raw data is presented, based on Rissanen's minimal description length (MDL) principle, which can learn unrestricted multiply‐connected belief networks and allows for trade off accuracy and complexity in the learned model.
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