Published:
May 2000
Proceedings:
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000)
Volume
Issue:
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000)
Track:
All Papers
Downloads:
Abstract:
The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a design sample size that increases exponentially with the number of dimensions. A method was developed that combines classifications from marginal density functions using an additional classifier. Unlike voting methods, this method can select a more appropriate class than the ones selected by the marginal classifiers, thus "overriding" their decisions. For two classes and two features, this method always demonstrates a probability of error no worse than the probability of error of the best marginal classifier.
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FLAIRS
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000)
ISBN 978-1-57735-113-9
Published by The AAAI Press, Menlo Park, California.