Performance Increasing Methods for Probabilistic Neural Networks
Bülent Bölat, Tülay Yıldırım · Information Technology Journal · 2003
Through this paper, some performance increasing methods for probabilistic neural network (PNN) are presented.These methods are tested with the glass benchmark database which has an irregular class distribution.Selection of a good training dataset is one of the most important issue.Therefore, a new data selection procedure was proposed.A data replication method is applied to the rare events of the dataset.After reaching the best accuracy, a principal component analysis (PCA) is used to reduce the computational complexity of PNN.Better classification accuracy than the reference work using Bayesian EM model was achieved by PNN using these methods.