Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric
| Contribuinte(s) |
Cercone, Nick Lin, T.Y. Wu, Xindong |
|---|---|
| Data(s) |
01/01/2001
|
| Resumo |
This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.<br /> |
| Identificador | |
| Idioma(s) |
eng |
| Publicador |
IEEE Computer Society |
| Relação |
http://dro.deakin.edu.au/eserv/DU:30004630/li-evolutionarystructure-2001.pdf http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989580 |
| Direitos |
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| Tipo |
Conference Paper |
