A novel adaptive resource-aware PNN algorithm based on Michigan-nested Pittsburgh PSO

Abstract

The computational and power resource limitations applicable to intelligent sensor systems in mobile implementations have gained much attention for industrial and medical applications. Probabilistic Neural Networks (PNN) are one of a successful classifier used to solve many classification problems. Currently, in PNN all patterns of training set are used to estimate the probability density function (pdf) of a given class as the sum of isotropic Gaussian kernels. However, the computational effort and the storage requirement of PNN method will prohibitively increase as the number of patterns used in the training set increases. In this paper, we propose as a remedy an Adaptive Resource-Aware Probabilistic Neural Networks (ARAPNN) based on two optimization goals tackle by Particle Swarm Optimization (PSO), which are finding the proper position and number of prototypes (Michigan approach) as well as the best smoothing factor σ (Pittsburgh approach). Our proposed algorithm was be tested with five benchmark data sets. The results show that the ARAPNN is able to find solutions with significantly reduced number of prototypes that classify data with competitive or better accuracy than the original PNN and Nearest Neighbor classifiers. © 2009 Springer Berlin Heidelberg.