Semigroup Forum

, 78:293

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

Research Article


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.


Brandt semigroups Classification Data mining 

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • A. V. Kelarev
    • 1
  • J. L. Yearwood
    • 1
  • M. A. Mammadov
    • 1
  1. 1.School of Information Technology and Mathematical SciencesUniversity of BallaratBallaratAustralia

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