Abstract
Probabilistic neural network (PNN) consists of the number of pattern neurons that equals the cardinality of the data set. The model design is therefore complex for large database classification problems. In this article, two effective PNN reduction procedures are introduced. In the first approach, the PNN’s pattern layer neurons are reduced by means of a k-means clustering procedure. The second method uses a support vector machines algorithm to select pattern layer nodes. Modified PNN networks are compared with the original model in medical data classification problems. The prediction ability expressed in terms of the 20% test set error for the networks is assessed. By means of the experiments, it is shown that the appropriate pruning of the pattern layer neurons in the PNN enhances the performance of the classifier.
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Kusy, M., Kluska, J. (2013). Probabilistic Neural Network Structure Reduction for Medical Data Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_11
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DOI: https://doi.org/10.1007/978-3-642-38658-9_11
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