Neural Computing & Applications

, Volume 9, Issue 2, pp 124–132

Feature Selection Using Probabilistic Neural Networks

  • A. Hunter

DOI: 10.1007/s005210070023

Cite this article as:
Hunter, A. NCA (2000) 9: 124. doi:10.1007/s005210070023

Selection of input variables (features) is a key stage in building predictive models. As exhaustive evaluation of potential feature sets using full non-linear models is impractical, it is common practice to use simple fast-evaluating models and heuristic selection strategies. This paper discusses a fast, efficient, and powerful non-linear input selection procedure using a combination of probabilistic neural networks and repeated bitwise gradient descent with resampling. The algorithm is compared with forward selection, backward selection and genetic algorithms using a selection of real-world data sets. The algorithm has comparative performance and greatly reduced execution time with respect to these alternative approaches.

Keywords:Feature selection; Probabilistic neural networks 

Copyright information

© Springer-Verlag London Limited 2000

Authors and Affiliations

  • A. Hunter
    • 1
  1. 1.Department of Computing and Engineering Technology, University of Sunderland, Sunderland, UKGB

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