Optimization of neural classifiers based on Bayesian decision boundaries and idle neurons pruning

Abstract

In this article we describe a feature extraction algorithm for pattern classification based on Bayesian decision boundaries and pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that really contribute to correct classification. Also, we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method.