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

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/11494683_34
AuthorSearch for: 1; Search for: 1; Search for: 1
Affiliation
  1. National Research Council of Canada. NRC Institute for Biodiagnostics
FormatText, Book Chapter
Conference6th International Workshop on Multiple Classifier Systems (MCS 2005), June 13-15, 2005, California, USA
Subjectrandom subspace method; biomedical spectra; feature selection; feature extraction; domain knowledge; PCA
Abstract
Spectra 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
In
  • Multiple Classifier Systems: 6th International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005. Proceedings (2005): 336–345.
Series
  • Lecture Notes in Computer Science, no. 3541.
LanguageEnglish
Peer reviewedYes
NRC numberNRC-IBD-2212
NPARC number9147903
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Record identifier9143a01f-edcf-41f1-b6ae-44acfccf8b99
Record created2009-06-25
Record modified2020-06-16
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