- Title
- A formula for multiple classifiers in data mining based on Brandt semigroups
- Creator
- Kelarev, Andrei; Yearwood, John; Mammadov, Musa
- Date
- 2009
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/58566
- Identifier
- vital:1781
- Identifier
-
https://doi.org/10.1007/s00233-008-9098-9
- Identifier
- ISSN:0037-1912
- Abstract
- 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. © 2008 Springer Science+Business Media, LLC.
- Publisher
- Springer
- Relation
- Semigroup Forum Vol. 78, no. 2 (2009), p. 293-309
- Rights
- Copyright Springer
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Brandt semigroups; Classification; Data mining
- Full Text
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