A formula for multiple classifiers in data mining based on Brandt semigroups
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- Kelarev, A.V., Yearwood, J.L. & Mammadov, M.A. Semigroup Forum (2009) 78: 293. doi:10.1007/s00233-008-9098-9
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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.