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
Akaike's information criterion and recent developments in information complexity
Journal of Mathematical Psychology
Model selection based on minimum description length
Journal of Mathematical Psychology
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Knowledge and Data Engineering
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
A Parsimonious Constraint-based Algorithm to Induce Bayesian Network Structures from Data
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
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
Learning scene entries and exits using coherent motion regions
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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The Bayesian Information Criterion (BIC) and the Minimum Description Length Principle (MDL) have been widely proposed as good metrics for model selection. Such scores basically include two terms: one for accuracy and the other for complexity. Their philosophy is to find a model that rightly balances these terms. However, it is surprising that both metrics do often not work very well in practice for they overfit the data. In this paper, we present an analysis of the BIC and MDL scores using the framework of Bayesian networks that supports such a claim. To this end, we carry out different tests that include the recovery of gold-standard network structures as well as the construction and evaluation of Bayesian network classifiers. Finally, based on these results, we discuss the disadvantages of both metrics and propose some future work to examine these limitations more deeply.