Distribution-free Learning of Bayesian Network Structure
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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. |
| Links: | |
| 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 |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Berlin, Germany |
| 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 |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{5253,
title = {Distribution-free Learning of Bayesian Network Structure},
journal = {Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008},
booktitle = {ECML PKDD 2008},
abstract = {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.},
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},
author = {Sun, X.},
doi = {10.1007/978-3-540-87481-2_28},
month_numeric = {9}
}