Semigroup Forum

, 78:293

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

Authors

    • School of Information Technology and Mathematical SciencesUniversity of Ballarat
  • J. L. Yearwood
    • School of Information Technology and Mathematical SciencesUniversity of Ballarat
  • M. A. Mammadov
    • School of Information Technology and Mathematical SciencesUniversity of Ballarat
Research Article

DOI: 10.1007/s00233-008-9098-9

Cite this article as:
Kelarev, A.V., Yearwood, J.L. & Mammadov, M.A. Semigroup Forum (2009) 78: 293. doi:10.1007/s00233-008-9098-9

Abstract

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.

Keywords

Brandt semigroupsClassificationData mining

Copyright information

© Springer Science+Business Media, LLC 2008