Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction
Machine Learning - Special issue: Unsupervised learning
A framework for context-sensitive metadata description
International Journal of Metadata, Semantics and Ontologies
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Bayesian metanetwork for context-sensitive feature relevance
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of conditional dependency. However, depending on a context, many attributes of the model might not be relevant. If a Bayesian Network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on "relevance" of the predictive attributes towards target attributes. In this paper we use the Bayesian Metanetwork vision to model context-sensitive feature relevance. Separating contextual and predictive features is an important task. In this paper we also consider three strategies of extracting context from relevant features, which are based on: part_of context, role-based context and interface-based context.