Decision combination of multiple classifiers

Document Type

Article

Publication Date

3-1-2008

Abstract

In order to improve the performance in pattern classification, we utilize multiple classifiers and combine their individual decisions to make a final decision. In this paper, we present the combination using Bayesian method and compare minimum errors. This method requires the posteriori probabilities from all classifiers, which may be difficult to calculate in real world because tremendous amounts of training samples are needed. Alternatively, a confusion matrix is developed for approximation. We also use different combining rules for comparisons and apply them to handwritten digit recognition. © 2008 World Scientific Publishing Company.

Identifier

44349142719 (Scopus)

Publication Title

International Journal of Pattern Recognition and Artificial Intelligence

External Full Text Location

https://doi.org/10.1142/S0218001408006223

ISSN

02180014

First Page

323

Last Page

334

Issue

2

Volume

22

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