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On Feature Selection with Measurement Cost and Grouped Features

  • Pavel Paclík
  • Robert P. W. Duin
  • Geert M. P. van Kempen
  • Reinhard Kohlus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

Feature selection is an important tool reducing necessary feature acquisition time in some applications. Standard methods, proposed in the literature, do not cope with the measurement cost issue. Including the measurement cost into the feature selection process is difficult when features are grouped together due to the implementation. If one feature from a group is requested, all others are available for zero additional measurement cost. In the paper, we investigate two approaches how to use the measurement cost and feature grouping in the selection process. We show, that employing grouping improves the performance significantly for low measurement costs. We discuss an application where limiting the computation time is a very important topic: the segmentation of backscatter images in product analysis.

Keywords

Feature Selection Discrete Cosine Transform Feature Subset Feature Selection Algorithm Measurement Cost 
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.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Pavel Paclík
    • 1
  • Robert P. W. Duin
    • 1
  • Geert M. P. van Kempen
    • 2
  • Reinhard Kohlus
    • 2
  1. 1.Pattern Recognition GroupDelft University of TechnologyThe Netherlands
  2. 2.Unilever R&D VlaardingenThe Netherlands

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