Comparison of Back Propagation and Resilient Propagation Algorithm for Spam Classification

Abstract

In this paper we compare the performance of back propagation and resilient propagation algorithms in training neural networks for spam classification. Back propagation algorithm is known to have issues such as slow convergence, and stagnation of neural network weights around local optima. Researchers have proposed resilient propagation as an alternative. Resilient propagation and back propagation are very much similar except for the weight update routine. Resilient propagation does not take into account the value of the partial derivative (error gradient), but rather considers only the sign of the error gradient to indicate the direction of the weight update. We show that resilient propagation yields faster convergence and higher accuracy on the UCI Spambase dataset.