A parsimonious constraint-based algorithm to induce Bayesian network structures from data

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...

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Bibliographic Details
Main Authors: Cruz-Ramirez, N., Nava-Fernandez, L., Mesa, H.G.A., Martinez, E.B., Rojas-Marcial, J.E.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Artificial intelligence
Bayesian methods
Entropy
Probability distribution
Random variables
Search engines
Simulated annealing
Statistical analysis
Testing
Online Access:Request full text
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Summary: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 generalizes well in a wide range of situations.
ISSN:1550-4069
2332-5712
DOI:10.1109/ENC.2005.6