Weighted probabilistic neural network

In this work, the modification of the probabilistic neural network (PNN) is proposed. The traditional network is adjusted by introducing the weight coefficients between pattern and summation layer. The weights are derived using the sensitivity analysis (SA) procedure. The performance of the weighted PNN (WPNN) is examined in data classification problems on benchmark data sets. The obtained WPNN's efficiency results are compared with these achieved by a modified PNN model put forward in literature, the original PNN and selected state-of-the-art classification algorithms: support vector machine, multilayer perceptron, radial basis function neural network, k-nearest neighbor method and gene expression programming algorithm. All classifiers are collated by computing the prediction accuracy obtained with the use of a k-fold cross validation procedure. It is shown that in seven out of ten classification cases, WPNN outperforms both the weighted PNN classifier introduced in literature and the original model. Furthermore, according to the ranking statistics, the proposed WPNN takes the first place among all tested algorithms.