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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5543

Title: Binary naive Bayesian classifiers for correlated Gaussian features: a theoretical analysis
Authors: Van Dyk, E
Barnard, E
Keywords: Naive Bayesian classifiers
Gaussian features
Pattern recognition
PRASA 2008
Issue Date: Nov-2008
Publisher: PRASA 2008
Citation: Van Dyk, E and Barnard, E. 2008. Binary naive Bayesian classifiers for correlated Gaussian features: a theoretical analysis. Nineteenth Annual Symposium of the Pattern Recognition Association of South Africa (PRASA 2008), Cape Town, South Africa, 27-28 November 2008
Abstract: We investigate the use of Naive Bayesian classifiers for correlated Gaussian feature spaces and derive error estimates for these classifiers. The error analysis is done by developing an exact expression for the error performance of a binary classifier with Gaussian features while using any quadratic decision boundary. Therefore, the analysis is not restricted to Naive Bayesian classifiers alone and can, for instance, be used to calculate the Bayes error performance. We compare the analytical error rate to that obtained when Monte-Carlo simulations are performed for a 2 and 12 dimensional binary classification problem. Finally, we illustrate the robust performances obtained with Naive Bayesian classifiers (as opposed to a maximum likelihood classifier) for high dimensional problems when data sparsity becomes an issue.
Description: Nineteenth Annual Symposium of the Pattern Recognition Association of South Africa (PRASA 2008), Cape Town, South Africa, 27-28 November 2008
URI: http://hdl.handle.net/10204/5543
ISBN: 9780799223507
Appears in Collections:Human language technologies
General science, engineering & technology

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