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Optimization of multiple classifiers in data mining based on string rewriting systems

By Richard Dazeley, Andrei Kelarev, John Yearwood and Musa Mammadov

Abstract

Optimization of multiple classifiers is an important problem in data mining. We introduce additional structure on the class sets of the classifiers using string rewriting systems with a convenient matrix representation. The aim of the present paper is to develop an efficient algorithm for the optimization of the number of errors of individual classifiers, which can be corrected by these multiple classifiers

Topics: Optimization, Multiple classifiers, String rewriting systems
Year: 2009
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