A formula for multiple classifiers in data mining based on Brandt semigroups

A general approach to designing multiple classifiers represents them as a combination of several binary classifiers in order to enable correction of classification errors and increase reliability. This method is explained, for example, in Witten and Frank (Data Mining: Practical Machine Learning Too...

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Bibliographic Details
Published inSemigroup forum Vol. 78; no. 2; pp. 293 - 309
Main Authors Kelarev, A. V., Yearwood, J. L., Mammadov, M. A.
Format Journal Article
LanguageEnglish
Published New York Springer-Verlag 01.03.2009
Subjects
Algebra
Mathematics
Mathematics and Statistics
Research Article
Brandt semigroups
Data mining
Classification
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Summary:A general approach to designing multiple classifiers represents them as a combination of several binary classifiers in order to enable correction of classification errors and increase reliability. This method is explained, for example, in Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques, 2005, Sect. 7.5). The aim of this paper is to investigate representations of this sort based on Brandt semigroups. We give a formula for the maximum number of errors of binary classifiers, which can be corrected by a multiple classifier of this type. Examples show that our formula does not carry over to larger classes of semigroups.
ISSN:0037-1912
1432-2137
DOI:10.1007/s00233-008-9098-9