Performance Evaluation of Classifiers Created using Elman Back-Propagation and Cascade Feed-forward Neural Networks
Abstract:Abstract
Huge data being captured in our
day-to-day activities are mostly imbalance. Such data therefore, calls for
fast, accurate and robust techniques, through which they could be analyzed in
order to fast-track early decision making. A Cascade Feed-forward Neural
Networks and Elman Backpropagation are known techniques in neural network
domain and their efficacies is therefore tested on separable data in this
study. The objective of this study is to evaluate the performance of these
techniques in solving a linear classification problem. The linear
classification of data involves, splitting of separable data into two distinct
clusters. In order to achieve the goal of this study, linear classifiers were
created using the two aforementioned techniques. Both network structures were
exposed to the same dataset and similar parameter configurations were set for
each technique. The model that emanates through each technique was simulated
using the set of untrained data. In order to determine the accuracy of each
model created through each technique, their Mean Absolute Errors (MAE) was
computed. The performance of each model was determined based on the value of
MAE. The error computation for the simulated output reveals that, cascade
neural network gives an error of 0.0928; while the model created using Elman
Backpropagation network gives a relatively lower error of 0.0661. It can be
inferred from this study, that, both techniques are capable of fitting accurate
classifiers from dataset and specifically, both techniques are very suitable
for binary classification of separable data.