Using Domain Knowledge in the Random Subspace Method: Application to the Classification of Biomedical Spectra

  • Erinija Pranckeviciene
  • Richard Baumgartner
  • Ray Somorjai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3541)

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.

Keywords

Random Subspace Method biomedical spectra feature selection feature extraction domain knowledge PCA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Erinija Pranckeviciene
    • 1
    • 2
  • Richard Baumgartner
    • 1
  • Ray Somorjai
    • 1
  1. 1.Institute for BiodiagnosticsNational Research Council CanadaWinnipegCanada
  2. 2.Kaunas University of TechnologyKaunasLithuania

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