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Improvement of classification accuracy for stochastic discrimination - Multi-class classification

Zong, N. and Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 (2004) Improvement of classification accuracy for stochastic discrimination - Multi-class classification. In: Chu, H. W., Savoie, M. and Sanchez, B. (eds.) International Conference on Computing, Communications and Control Technologies, Vol 2, Proceedings. Int Inst Informatics & Systemics, Orlando, pp. 17-22. ISBN 9806560175

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Abstract/Summary

In this paper, an improved stochastic discrimination (SD) is introduced to reduce the error rate of the standard SD in the context of multi-class classification problem. The learning procedure of the improved SD consists of two stages. In the first stage, a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. In the second stage, the standard SD is modified by (i) restricting sampling in the important space; and (ii) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but smaller variance than that of standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples axe provided to demonstrate the effectiveness of the proposed improved SD.

Item Type:Book or Report Section
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:14519
Uncontrolled Keywords:stochastic discrimination, multiclass classification, CLASSIFIERS
Publisher:Int Inst Informatics & Systemics

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