Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction
Machine Learning - Special issue: Unsupervised learning
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Learning with mixtures of trees
The Journal of Machine Learning Research
On supervised selection of Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Different Bayesian network models in the classification of remote sensing images
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Learning Bayesian network classifiers by risk minimization
International Journal of Approximate Reasoning
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A Bayesian multinet classifier allows a different set of independence assertions among variables in each of a set of local Bayesian networks composing the multinet. The structure of the local network is usually learned using a joint-probability-based score that is less specific to classification, i.e., classifiers based on structures providing high scores are not necessarily accurate. Moreover, this score is less discriminative for learning multinet classifiers because generally it is computed using only the class patterns and avoiding patterns of the other classes. We propose the Bayesian class-matched multinet (BCM2) classifier to tackle both issues. The BCM2 learns each local network using a detection-rejection measure, i.e., the accuracy in simultaneously detecting class patterns while rejecting patterns of the other classes. This classifier demonstrates superior accuracy to other state-of-the-art Bayesian network and multinet classifiers on 32 real-world databases.