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On the problem of dimensionality and sample size in multi-stage pattern classifiers

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1 Author(s)
Dante, H. ; University of the West Indies, St. Augustine, Trinidad

In practical pattern recognition problems, the underlying probability distributions are not known a priori, but have to be estimated using finite number of labelled samples. It is well known that under such situations the Bayes classifier has a degrading performance when the number of features exceeds an optimal value. In this paper we study the possibility of using different classification procedures which use a subset of the available features at a step in an effort to circumvent the dimensionality problem. The classification schemes studied are the majority decision scheme and the decision tree classifier for normal populations.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '84.  (Volume:9 )

Date of Conference:

Mar 1984