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Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes (2006)
| Venue: | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING |
| Citations: | 10 - 0 self |
BibTeX
@ARTICLE{Pérez06supervisedclassification,
author = {Aritz Pérez and Pedro Larrañaga and Iñaki Inza},
title = {Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes},
journal = {INTERNATIONAL JOURNAL OF APPROXIMATE REASONING},
year = {2006},
volume = {43},
pages = {2006}
}
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Abstract
Most of the Bayesian network-based classifiers are usually only able to handle discrete variables. However, most real-world domains involve continuous variables. A common practice to deal with continuous variables is to discretize them, with a subsequent loss of information. This work shows how discrete classifier induction algorithms can be adapted to the conditional Gaussian network paradigm to deal with continuous variables without discretizing them. In addition, three novel classifier induction algorithms and two new propositions about mutual information are introduced. The classifier induction algorithms presented are ordered and grouped according to their structural complexity: naive Bayes, tree augmented naive Bayes, k-dependence Bayesian classifiers and semi naive Bayes. All the classifier induction algorithms are empirically evaluated using predictive accuracy, and they are compared to linear discriminant analysis, as a continuous classic statistical benchmark classifier. Besides, the accuracies for a set of state-of-the-art classifiers are included in order to justify the use of linear discriminant analysis as the benchmark algorithm. In order to understand the behavior of the conditional Gaussian network-based classifiers better, the results include bias-variance decomposition of the expected misclassification rate. The study suggests that semi naive Bayes structure based classifiers and, especially, the novel wrapper condensed semi naive Bayes backward, outperform the behavior of the rest of the presented classifiers. They also obtain quite competitive results compared to the state-of-the-art algorithms included.
Keyphrases
naive bayes conditional gaussian network induction algorithm structure complexity continuous variable semi naive bayes k-dependence bayesian classifier competitive result state-of-the-art classifier conditional gaussian network-based classifier novel wrapper conditional gaussian network paradigm bias-variance decomposition structural complexity predictive accuracy discrete variable linear discriminant analysis bayesian network-based classifier benchmark algorithm expected misclassification rate state-of-the-art algorithm discriminant analysis common practice bayesian network mutual information semi naive bayes backward new proposition key word real-world domain semi naive bayes structure presented classifier subsequent loss continuous classic statistical benchmark classifier
