Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Functional Brain Imaging of Young, Nondemented, and Demented Older Adults
Journal of Cognitive Neuroscience
Shrinkage Estimator for Bayesian Network Parameters
ECML '07 Proceedings of the 18th European conference on Machine Learning
Feature selection for Bayesian network classifiers using the MDL-FS score
International Journal of Approximate Reasoning
Effective connectivity analysis of fMRI and MEG data collected under identical paradigms
Computers in Biology and Medicine
Improving bayesian network structure search with random variable aggregation hierarchies
ECML'06 Proceedings of the 17th European conference on Machine Learning
Sampling of virtual examples to improve classification accuracy for nominal attribute data
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
| Hi-index | 0.00 |
In many domains, a Bayesian network's topological structure is not known a priori and must be inferred from data. This requires a scoring function to measure how well a proposed network topology describes a set of data. Many commonly used scores such as BD, BDE, BDEU, etc., are not well suited for class discrimination. Instead, scores such as the class-conditional likelihood (CCL) should be employed. Unfortunately, CCL does not decompose and its application to large domains is not feasible. We introduce a decomposable score, approximate conditional likelihood (ACL) that is capable of identifying class discriminative structures. We show that dynamic Bayesian networks (DBNs) trained with ACL have classification efficacies competitive to those trained with CCL on a set of simulated data experiments. We also show that ACL-trained DBNs outperform BDE-trained DBNs, Gaussian naïve Bayes networks and support vector machines within a neuroscience domain too large for CCL.