, Volume 78, Issue 2, pp 293-309
Date: 06 Aug 2008

A formula for multiple classifiers in data mining based on Brandt semigroups

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


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.

Communicated by Thomas E. Hall.