The complexity of Boolean functions
The complexity of Boolean functions
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Noisy-or classifier: Research Articles
International Journal of Intelligent Systems - Uncertainty Processing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Bayesian network modelling through qualitative patterns
Artificial Intelligence
Structure and parameter learning for causal independence and causal interaction models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Improving the therapeutic performance of a medical bayesian network using noisy threshold models
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Predicting carcinoid heart disease with the noisy-threshold classifier
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
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Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric Boolean function. The developed algorithm enables us to assess the practical usefulness of the symmetric causal independence models, which has not been done previously. We evaluate the classification performance of the symmetric causal independence models learned with the presented EM algorithm. The results show the competitive performance of these models in comparison to noisy OR and noisy AND models as well as other state-of-the-art classifiers.