caa a22 4500 475834119 CHVBK 20180406123849.0 cr unu---uuuuu 170329e20000701xx s 000 0 eng 10.1007/s005210070023 doi (NATIONALLICENCE)springer-10.1007/s005210070023 Hunter A. Department of Computing and Engineering Technology, University of Sunderland, Sunderland, UK, GB aut Feature Selection Using Probabilistic Neural Networks [Elektronische Daten] [A. Hunter] 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.: Springer-Verlag London Limited, 2000 Keywords:Feature selection nationallicence Probabilistic neural networks nationallicence https://doi.org/10.1007/s005210070023 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s005210070023 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Hunter A. Department of Computing and Engineering Technology, University of Sunderland, Sunderland, UK, GB aut Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer