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Combination of multiple classifiers by minimizing the upper bound of bayes error rate for unconstrained handwritten numeral recognition

Authors
Kang, HJLee, SW
Issue Date
5월-2005
Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
Keywords
combination of multiple classifiers; Bayes error rate; dependency; approximation scheme; mutual information; handwritten numeral recognition
Citation
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.19, no.3, pp.395 - 413
Indexed
SCIE
SCOPUS
Journal Title
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume
19
Number
3
Start Page
395
End Page
413
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/123239
DOI
10.1142/S0218001405004101
ISSN
0218-0014
Abstract
In order to raise a class discrimination power by the combination of multiple classifiers, the upper bound of Bayes error rate which is bounded by the conditional entropy of a class and decisions should be minimized. Based on the minimization of the upper bound of the Bayes error rate, Wang and Wong proposed only a tree dependence approximation scheme of a high-dimensional probability distribution composed of a class and patterns. This paper extends such a tree dependence approximation scheme to higher order dependency for improving the classification performance and thus optimally approximates the high-dimensional probability distribution with a product of low-dimensional distributions. And then, a new combination method by the proposed approximation scheme is presented and evaluated with classifiers recognizing unconstrained handwritten numerals.
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