Learning Local Components to Understand Large Bayesian Networks
Publikation: Forskning - peer review › Konferenceartikel i proceeding
Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data.
|Titel||IEEE International Conference on Data Mining (ICDM 2009)|
|Udgiver||IEEE Computer Society Press|
|Konference||IEEE International Conference on Data Mining (ICDM 2009)|
|Periode||06-12-09 → 09-01-10|
|Navn||Proceedings of the 2009 Ninth IEEE International Conference on Data Mining|