Bagging and the Random Subspace Method for Redundant Feature Spaces

  • Marina Skurichina
  • Robert P. W. Duin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)

Abstract

The performance of a single weak classifier can be improved by using combining techniques such as bagging, boosting and the random subspace method. When applying them to linear discriminant analysis, it appears that they are useful in different situations. Their performance is strongly affected by the choice of the base classifier and the training sample size. As well, their usefulness depends on the data distribution. In this paper, on the example of the pseudo Fisher linear classifier, we study the effect of the redundancy in the data feature set on the performance of the random subspace method and bagging.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Marina Skurichina
    • 1
  • Robert P. W. Duin
    • 1
  1. 1.Pattern Recognition Group, Department of Applied Physics, Faculty of Applied SciencesDelft University of TechnologyDelftThe Netherlands

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