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

Authors:
Erinija Pranckeviciene;Richard Baumgartner;Ray Somorjai
Affiliations:
Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Canada;Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Canada;Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Canada
Venue:
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Year:
2005

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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.