Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Communications of the ACM
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Multidimensional Data Modeling for Complex Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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When adopting Bayesian network (BN) to represent and infer probabilistic causalities among multidimensional variables, the size of the conditional probability table (CPT) associated with each variable is doomed to be large, and the causality inferences cannot be done for arbitrary evidences. In this paper, we first extend the general BN by augmenting parameters for describing causalities among classes instead of specific instances of multidimensional variables. In the extended BN, called CBN, the CPT of a variable includes the probability of each class given parent classes, while a classifier of each variable is associated to determine the class that the given evidence belongs to. Further, we give the method for approximate inferences of the CBN for arbitrary evidences. Preliminary experiments verify the feasibility of our methods.