Evaluating Performance of Random Subspace Classifier on ELENA Classification Database

  • Dmitry Zhora
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3697)

Abstract

This work describes the model of random subspace classifier and provides benchmarking results on the ELENA database. The classifier uses a coarse coding technique to transform the input real vector into the binary vector of high dimensionality. Thus, class representatives are likely to become linearly separable. Taking into account the training time, recognition time and error rate the RSC network in many cases surpasses well known classification algorithms.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dmitry Zhora
    • 1
  1. 1.Institute of Software SystemsKievUkraine

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