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Feature selection using genetic algorithms and probabilistic neural networks

By Andrew Hunter

Abstract

Selection of input variables is a key stage in building\ud predictive models, and an important form of data mining. As exhaustive evaluation of potential input sets using full non-linear models is impractical, it is necessary to use simple fast-evaluating models and heuristic selection strategies. This paper discusses a fast, efficient, and powerful nonlinear input selection procedure using a combination of Probabilistic Neural Networks and repeated\ud bitwise gradient descent. The algorithm is compared\ud with forward elimination, backward elimination 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. It is demonstrated empirically that reliable results cannot be gained using any of these approaches without the use of resampling

Topics: G700 Artificial Intelligence, G760 Machine Learning, G730 Neural Computing
Publisher: Springer
Year: 2000
DOI identifier: 10.1007/s005210070023
OAI identifier: oai:eprints.lincoln.ac.uk:1913

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