A Z-Score Fuzzy Exponential Adaptive Skipping Training (Z-Feast) Algorithm for Efficient Pattern Classification

Abstract

Among the existing NN architectures, Multilayer Feedforward Neural Network (MFNN) with single hidden layer architecture has been scrutinized thoroughly as best for solving nonlinear classification problem. The training time is consumed more for very huge training datasets in the MFNN training phase. In order to reduce the training time, a simple and fast training algorithm called Exponential Adaptive Skipping Training (EAST) Algorithm was presented that improves the training speed by significantly reducing the total number of training input samples consumed by MFNN for training at every single epoch. Although the training performance of EAST achieves faster, it still lacks in the accuracy rate due to high skipping factor. In order to improve the accuracy rate of the training algorithm, Hybrid system has been suggested in which the neural network is trained with the fuzzified data. In this paper, a z-Score Fuzzy Exponential Adaptive Skipping Training (z-FEAST) algorithm is proposed which is based on the fuzzification of EAST. The evaluation of the proposed z-FEAST algorithm is demonstrated effectively using the benchmark datasets - Iris, Waveform, Heart Disease and Breast Cancer for different learning rate. Simulation study proved that z-FEAST training algorithm improves the accuracy rate.