Model selection
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Machine Learning - Special issue on learning with probabilistic representations
A tutorial on learning with Bayesian networks
Learning in graphical models
Learning Bayesian networks with local structure
Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
An introduction to model selection
Journal of Mathematical Psychology
Akaike's information criterion and recent developments in information complexity
Journal of Mathematical Psychology
Journal of Mathematical Psychology
Model selection based on minimum description length
Journal of Mathematical Psychology
Data mining: concepts and techniques
Data mining: concepts and techniques
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Wrappers for Performance Enhancements and Oblivious Decision Graphs.
Wrappers for Performance Enhancements and Oblivious Decision Graphs.
Learning bayesian networks from data
Learning bayesian networks from data
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Knowledge and Data Engineering
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Diagnosis of breast cancer using Bayesian networks: A case study
Computers in Biology and Medicine
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Diagnosis of chronic idiopathic inflammatory bowel disease using bayesian networks
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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In this paper, we present a novel algorithm, called MP-Bayes, which induces Bayesian network structures from data based on entropy measures. One of the main features of this method is its parsimonious nature: it tends to represent the joint probability distribution underlying the data with the least number of arcs. While other methods that build Bayesian networks tend to overfit the data, MP-Bayes creates models that seem to have an adequate trade-off between accuracy and complexity. To support such a claim, we compare the performance of MP-Bayes, in terms of classification, against those of four different Bayesian network classifiers. The results show that our procedure generalises well in a wide range of situations.