Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Convex Optimization
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning mixtures of DAG models
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Structure identification of Bayesian classifiers based on GMDH
Knowledge-Based Systems
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Bayesian network classifiers (BNC) have received considerable attention in machine learning field. Some special structure BNCs have been proposed and demonstrate promise performance. However, recent researches show that structure learning in BNs may lead to a non-negligible posterior problem, i.e, there might be many structures have similar posterior scores. In this paper, we propose a generalized additive Bayesian network classifiers, which transfers the structure learning problem to a generalized additive models (GAM) learning problem. We first generate a series of very simple BNs, and put them in the framework of GAM, then adopt a gradient-based algorithm to learn the combining parameters, and thus construct a more powerful classifier. On a large suite of benchmark data sets, the proposed approach outperforms many traditional BNCs, such as naive Bayes, TAN, etc, and achieves comparable or better performance in comparison to boosted Bayesian network classifiers.