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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...
Selecting relevant features is in demand when a large data set is of interest in a classification task. It produces a tractable number of features that are sufficient and possibly improve the classification performance. This paper studies a statistical method of Markov blanket induction algorithm for filtering features and then applies a classifier using the Markov blanket predictors. The Markov blanket...
Interactive influence diagrams (I-IDs) offer a transparent and semantically clear representation for the decision-making problem in multiagent settings. They ascribe procedural models such as IDs and I-IDs to the behavior of other agents. Procedural models offer the benefit of understanding how others arrive at their behaviors. However, as model spaces are often bounded, the true models of others...
Maintaining an accurate assessment of military situation assessment is essential to prevention, containment and effective response to military conflicts of all kinds. This research developed an intelligent decision support system for military situation assessment, which would be extremely useful for military decision makers. High uncertainty and the dynamics of the environment is a crucial problem...
Refinement of Bayesian network structures using new data becomes more and more relevant. Some work has been done there; however, one problem has not been considered yet - what to do when new data has fewer or more attributes than the existing model. In both cases data contains important knowledge and every effort must be made in order to extract it. In this paper, we propose a general merging algorithm...
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