High-resolution spectroscopy is a powerful industrial tool. The number of features (wavelengths) in these data sets varies from several hundreds up to a thousand. Relevant feature selection/extraction algorithms are necessary to handle data of such a large dimensionality. One of the possible solutions is the SVM shaving technique. It was developed for applications in microarray data, which also have a huge number of features. The fact that the neighboring features (wavelengths) are highly correlated allows one to propose the SVM band-shaving algorithm, which takes into account the prior knowledge on the wavelengths order. The SVM band-shaving has a lower computational demands than the standard SVM shaving and selects features organized into bands. This is preferable due to possible noise reduction and a more clear physical interpretation.


Feature Selection Method Band Extraction Lower Computational Demand Total Classification Error High Dimensional Microarray Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Serguei Verzakov
    • 1
  • Pavel Paclík
    • 1
  • Robert P. W. Duin
    • 1
  1. 1.Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands

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