Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
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
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Weightily averaged one-dependence estimators
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Analyzing the impact of the discretization method when comparing Bayesian classifiers
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Hi-index | 0.00 |
In this paper we present an extension to the classical k-dependence Bayesian network classifier algorithm. The original method intends to work for the whole continuum of Bayesian classifiers, from naïve Bayes to unrestricted networks. In our experience, it performs well for low values of k. However, the algorithm tends to degrade in more complex spaces, as it greedily tries to add k dependencies to all feature nodes of the resulting net. We try to overcome this limitation by seeking for optimal values of k on a feature per feature basis. At the same time, we look for the best feature ordering. That is, we try to estimate the joint probability distribution of optimal feature orderings and individual number of dependencies. We feel that this preserves the essence of the original algorithm, while providing notable performance improvements.