Optimization of classifiers for data mining based on combinatorial semigroups

A. V. Kelarev, J. L. Yearwood, P. A. Watters

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)242-251
Number of pages10
JournalSemigroup Forum
Issue number2
Publication statusPublished - Apr 2011
Externally publishedYes


  • Combinatorial semigroups
  • Data mining


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