Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Steps toward artificial intelligence
Computers & thought
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
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
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Machine Learning
Probabilistic decision graphs-combining verification and AI techniques for probabilistic inference
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Classification of gene functions using support vector machine for time-course gene expression data
Computational Statistics & Data Analysis
Localized empirical discriminant analysis
Computational Statistics & Data Analysis
Data mining based Bayesian networks for best classification
Computational Statistics & Data Analysis
Learning probabilistic decision graphs
International Journal of Approximate Reasoning
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
The PDG-Mixture Model for Clustering
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Structural-EM for learning PDG models from incomplete data
International Journal of Approximate Reasoning
Enhancing technology clustering through heuristics by using patent counts
Expert Systems with Applications: An International Journal
Modelling and inference with Conditional Gaussian Probabilistic Decision Graphs
International Journal of Approximate Reasoning
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A new model for supervised classification based on probabilistic decision graphs is introduced. A probabilistic decision graph (PDG) is a graphical model that efficiently captures certain context specific independencies that are not easily represented by other graphical models traditionally used for classification, such as the Naive Bayes (NB) or Classification Trees (CT). This means that the PDG model can capture some distributions using fewer parameters than classical models. Two approaches for constructing a PDG for classification are proposed. The first is to directly construct the model from a dataset of labelled data, while the second is to transform a previously obtained Bayesian classifier into a PDG model that can then be refined. These two approaches are compared with a wide range of classical approaches to the supervised classification problem on a number of both real world databases and artificially generated data.