Title :
Extracting local representations from large Bayesian networks
Author_Institution :
Sch. of Inf. Sci. & Eng., Yunnan Univ., Kunming
Abstract :
As Bayesian network models continue to grow larger and more complex, the efficiency of probabilistic inference and knowledge representations always cannot meet all demands. In fact, large applications always involve several fields and particular applications may only pay attention to a local domain in these large Bayesian networks within a long period of time. Motivated by this situation, this paper shifts attention to local representations, and concentrates on finding a method of extraction without loss of any information and free of dataset by capturing local graphical characterizations of large Bayesian networks. From the graphical perspective, this paper first captures two local graphical characterizations to determine the skeleton and V-structure of local models respectively. Moreover, several transformation rules are elicited from these local characterizations, and local representations can be obtained though applying these rules to the global network. The local representations can not only offer compact and intuitive representations for local domains concerned, but also allow evidence propagate in a smaller model other than the entire model.
Keywords :
Bayes methods; inference mechanisms; Bayesian networks; knowledge representations; local graphical characterizations; local representation extraction; probabilistic inference; Bayesian methods; Cybernetics; Data mining; Electronic mail; Inference algorithms; Information science; Knowledge representation; Machine learning; Object oriented modeling; Skeleton; Directed acyclic graph (DAG); graphical characterization; local representations; transformation rules;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620688