Efficient learning of Bayesian network classifiers: an extension to the TAN classifier

Authors:
Alexandra M. Carvalho;Arlindo L. Oliveira;Marie-France Sagot
Affiliations:
IST, TULisbon, INESC-ID, Lisboa, Portugal;IST, TULisbon, INESC-ID, Lisboa, Portugal;Inria Rhône-Alpes, Université Claude Bernard, Lyon I, France
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

Citing 14
Cited 1

The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Approximating probabilistic inference in Bayesian belief networks is NP-hard

Artificial Intelligence
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Modeling dependencies in protein-DNA binding sites

RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Learning Bayesian network classifiers by maximizing conditional likelihood

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Non-additivity in protein--DNA binding

Bioinformatics
Full Bayesian network classifiers

ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning Bayesian Networks Consistent with the Optimal Branching

ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
A feature-based approach to modeling protein-DNA interactions

RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Simple Bayesian classifiers do not assume independence

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Learning polytrees

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce a Bayesian network classifier less restrictive than Naive Bayes (NB) and Tree Augmented Naive Bayes (TAN) classifiers. Considering that learning an unrestricted network is unfeasible the proposed classifier is confined to be consistent with the breadth-first search order of an optimal TAN. We propose an efficient algorithm to learn such classifiers for any score that decompose over the network structure, including the well known scores based on information theory and Bayesian scoring functions. We show that the induced classifier always scores better than or the same as the NB and TAN classifiers. Experiments on modeling transcription factor binding sites show that, in many cases, the improved scores translate into increased classification accuracy.