Evaluating performance of random subspace classifier on ELENA classification database

Authors:
Dmitry Zhora
Affiliations:
Institute of Software Systems, Kiev, Ukraine
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

Citing 2
Cited 1

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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.