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)


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.


Probabilistic Neural Network Classification Database Classifier Architecture Discriminant Factorial Analysis Threshold Neuron 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kussul, E.M., Baidyk, T.N., Lukovich, V.V., Rachkovskij, D.A.: Adaptive high performance classifier based on random threshold neurons. In: Trappl, R. (ed.) Cybernetics and Systems, pp. 1687–1695. World Scientific Publishing Co. Pte. Ltd., Singapore (1994)Google Scholar
  2. 2.
    Kussul, E.M., Rachkovskij, D.A., Wunsch, D.C.: The random subspace coarse coding sche-me for real-valued vectors. In: Proc. Int. Joint Conf. Neural Networks, vol. 1, pp. 450–455 (1999)Google Scholar
  3. 3.
    Rachkovskij, D.A., Kussul, E.M.: Binding and normalization of binary sparse distributed representations by context-dependent thinning. Neural Computation 13(2), 411–452 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Zhora, D.V.: Random threshold classifier functioning analysis. Cybernetics and System Analysis, Kiev 3, 72–91 (2003); in Russian, Available: Google Scholar
  5. 5.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn., p. 654. Wiley Interscience, Hoboken (2000)Google Scholar
  6. 6.
    Aviles-Cruz, C., Guérin-Dugué, A., Voz, J.L., Van Cappel, D.: Databases, Enhanced Learning for Evolutive Neural Architecture. Tech. Rep. R3-B1-P, INPG, UCL, TSA, 47 (1995), Available:
  7. 7.
    Blayo, F., Cheneval, Y., Guérin-Dugué, A., Chentouf, R., Aviles-Cruz, C., Madrenas, J., Moreno, M., Voz, J.L.: Benchmarks, Enhanced Learning for Evolutive Neural Architecture. Tech. Rep. R3-B4-P, INPG, EERIE, EPFL, UPC, UCL, 114 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

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

Personalised recommendations