Comparison of two classification methodologies on a real-world biomedical problem

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DOIResolve DOI: http://doi.org/10.1007/3-540-70659-3_45
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TypeBook Chapter
Proceedings titleStructural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshops SSPR 2002 and SPR 2002 Windsor, Ontario, Canada, August 6–9, 2002 Proceedings
Series titleLecture Notes In Computer Science; Volume 2396
ConferenceJoint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 2002) and Statistical Pattern Recognition (SPR 2002), August 6–9, 2002, Windsor, Ontario, Canada
ISSN0302-9743
ISBN978-3-540-44011-6
978-3-540-70659-5
Volume2396
Pages433441; # of pages: 9
AbstractWe compare two diverse classification strategies on real-life biomedical data. One is based on a genetic algorithm-driven feature extraction method, combined with data fusion and the use of a simple, single classifier, such as linear discriminant analysis. The other exploits a single layer perceptron-based, data-driven evolution of the optimal classifier, and data fusion. We discuss the intricate interplay between dataset size, the number of features, and classifier complexity, and suggest different techniques to handle such problems.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNational Research Council Canada; NRC Institute for Biodiagnostics
Peer reviewedYes
NRC number1925
NPARC number9147594
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Record identifier5cd15de8-9fd8-490a-94ed-3267ac0e6a20
Record created2009-06-25
Record modified2016-06-21
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