Preprocessing of training set for backpropagation algorithm: histogram equalization

This paper introduces a data preprocessing algorithm that can improve the efficiency of the standard backpropagation (BP) algorithm. The basic approach is transforming input data to a range that associates high-slopes of sigmoid where relatively large modification of weights occurs. This helps escaping of early trapping from prematured saturation. However, a simple and uniform transformation to such desired range can lead to a slow learning if the data have a heavily skewed distribution. In order to improve the performance of BP algorithm on such distribution, the authors propose a modified histogram equalization technique which enhances the spacing between data points in the heavily concentrated regions of skewed distribution. The authors´ simulation study shows that this modified histogram equalization can significantly speed up the BP training as well as improving the generalization capability of the trained network