Modification of the Probabilistic Neural Network with the Use of Sensitivity Analysis Procedure
Maciej Kusy, Piotr Andrzej Kowalski
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 97–103 (2016)
Abstract. In this article, the modified probabilistic neural network (MPNN) is proposed. The network is an extension of conventional PNN with the weight coefficients introduced between pattern and summation layer of the model. These weights are calculated by using the sensitivity analysis (SA) procedure. MPNN is employed to the classification tasks and its performance is assessed on the basis of prediction accuracy. The effectiveness of MPNN is also verified by analyzing its results with these obtained for both the original PNN and commonly known classification algorithms: support vector machine, multilayer perceptron, radial basis function network and k-Means clustering procedure. It is shown that the proposed modification improves the prediction ability of the PNN classifier.
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