Using domain knowledge in the random subspace method : application to the classification of biomedical spectra

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DOIResolve DOI: http://doi.org/10.1007/11494683_34
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TypeBook Chapter
Proceedings titleMultiple Classifier Systems : 6th International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005. Proceedings
Series titleLecture Notes In Computer Science; Volume 3541
Conference6th International Workshop on Multiple Classifier Systems (MCS 2005), June 13-15, 2005, California, USA
ISSN0302-9743
Pages336345; # of pages: 10
SubjectRandom Subspace Method; biomedical spectra; feature selection; feature extraction; domain knowledge; PCA
AbstractSpectra intrinsically possess domain knowledge, making possible a domain-based feature selection model. The random subspace method, in combination with domain-knowledge-based feature sets, leads to improved classification accuracies in real-life biomedical problems. Using such feature sets allows for an efficient reduction of dimensionality, while preserving interpretability of classification outcomes, important for the field expert. We demonstrate the utility of domain knowledge-based features for the random subspace method for the classification of three real-life high-dimensional biomedical magnetic resonance (MR) spectra.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNRC Institute for Biodiagnostics; National Research Council Canada
Peer reviewedYes
NRC number2212
NPARC number9147903
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Record identifier9143a01f-edcf-41f1-b6ae-44acfccf8b99
Record created2009-06-25
Record modified2016-07-19
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