Optimization of classifiers for data mining based on combinatorial semigroups
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- Kelarev, A.V., Yearwood, J.L. & Watters, P.A. Semigroup Forum (2011) 82: 242. doi:10.1007/s00233-011-9298-6
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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.