Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
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
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
The revisiting problem in mobile robot map building: a hierarchical bayesian approach
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Uses of artificial intelligence in the Brazilian customs fraud detection system
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Hi-index | 0.00 |
We employed a multilevel hierarchical Bayesian model in the task of exploiting relevant interactions among high cardinality attributes in a classification problem without overfitting. With this model, we calculate posterior class probabilities for a pattern W combining the observations of W in the training set with prior class probabilities that are obtained recursively from the observations of patterns that are strictly more generic than W. The model achieved performance improvements over standard Bayesian network methods like Naive Bayes and Tree Augmented Naive Bayes, over Bayesian Networks where traditional conditional probability tables were substituted byNoisy-or gates, Default Tables, Decision Trees and Decision Graphs, and over Bayesian Networks constructed after a cardinality reduction preprocessing phase using the Agglomerative Information Bottleneck method.