Distribution-free Learning of Bayesian Network Structure
2008
Conference Paper
ei
We present an independence-based method for learning Bayesian network (BN) structure without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous domains. Even mixed continuous-categorical domains and structures containing vectorial variables can be handled. We address the problem by developing a non-parametric conditional independence test based on the so-called kernel dependence measure, which can be readily used by any existing independence-based BN structure learning algorithm. We demonstrate the structure learning of graphical models in continuous and mixed domains from real-world data without distributional assumptions. We also experimentally show that our test is a good alternative, in particular in case of small sample sizes, compared to existing tests, which can only be used in purely categorical or continuous domains.
Author(s): | Sun, X. |
Book Title: | ECML PKDD 2008 |
Journal: | Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008 |
Pages: | 423-439 |
Year: | 2008 |
Month: | September |
Day: | 0 |
Editors: | Daelemans, W. , B. Goethals, K. Morik |
Publisher: | Springer |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1007/978-3-540-87481-2_28 |
Event Name: | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
Event Place: | Antwerpen, Belgium |
Address: | Berlin, Germany |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @inproceedings{5253, title = {Distribution-free Learning of Bayesian Network Structure}, author = {Sun, X.}, journal = {Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008}, booktitle = {ECML PKDD 2008}, pages = {423-439}, editors = {Daelemans, W. , B. Goethals, K. Morik}, publisher = {Springer}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Berlin, Germany}, month = sep, year = {2008}, doi = {10.1007/978-3-540-87481-2_28}, month_numeric = {9} } |