- Title
- Optimization of classifiers for data mining based on combinatorial semigroups
- Creator
- Kelarev, Andrei; Yearwood, John; Watters, Paul
- Date
- 2011
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/69150
- Identifier
- vital:3898
- Identifier
- http://www.scopus.com/inward/record.url?eid=2-s2.0-79951853978&partnerID=40&md5=8b50070217ecaff107735df2869e7aad
- Identifier
- ISSN:0037-1912
- 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. © 2011 Springer Science+Business Media, LLC.
- Relation
- Semigroup Forum Vol. 82, no. 2 (2011), p. 1-10
- Rights
- Copyright Springer
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Combinatorial semigroups; Data mining
- Full Text
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