Predictive and Contextual Feature Separation for Bayesian Metanetworks

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.