Feature Selection Using Probabilistic Neural Networks
- 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
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