Algorithms for Learning Augmented Bayesian Classifiers

@inproceedings{Keogh1999AlgorithmsFL,
  title={Algorithms for Learning Augmented Bayesian Classifiers},
  author={Eamonn J. Keogh and Michael J. Pazzani},
  year={1999}
}
The naïve Bayes classifier is built on the assump tion of conditional independence between the attributes given the class . The algorithm has been shown to be surprisingly robust to obvious violations of thi s condition, but it is natural to ask if it is possible to further improve the accuracy by rela xing this assumption. We examine an approach where naïve Bayes is augmented by the addi tion of correlation arcs between attributes. We explore two methods for finding the set of augmenting arcs… CONTINUE READING

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