, Volume 82, Issue 2, pp 242-251
Date: 24 Feb 2011

Optimization of classifiers for data mining based on combinatorial semigroups

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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.

Communicated by Thomas E. Hall.