Semigroup Forum

, Volume 82, Issue 2, pp 242–251

Optimization of classifiers for data mining based on combinatorial semigroups

RESEARCH ARTICLE

DOI: 10.1007/s00233-011-9298-6

Cite this article as:
Kelarev, A.V., Yearwood, J.L. & Watters, P.A. Semigroup Forum (2011) 82: 242. doi:10.1007/s00233-011-9298-6

Abstract

The aim of the present article is to obtain a theoretical result essential for applications of combinatorial semigroups for the design of multiple classification systems in data mining. We consider a novel construction of multiple classification systems, or classifiers, combining several binary classifiers. The construction is based on combinatorial Rees matrix semigroups without any restrictions on the sandwich-matrix. Our main theorem gives a complete description of all optimal classifiers in this novel construction.

Keywords

Combinatorial semigroupsData mining

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • A. V. Kelarev
    • 1
  • J. L. Yearwood
    • 1
  • P. A. Watters
    • 1
  1. 1.School of Science, Information Technology and EngineeringUniversity of BallaratBallaratAustralia