Chebyshev Multilayer Perceptron Neural Network with Levenberg Marquardt-Back Propagation Learning for Classification Tasks

Artificial neural network has been proved among the best tools in data mining for classification tasks. Multilayer perceptron (MLP) neural network commonly used due to the fast convergence and easy implementation. Meanwhile, it fails to tackle higher dimensional problems. In this paper, Chebyshev multilayer perceptron neural network with Levenberg Marquardt back propagation learning is presented for classification task. Here, Chebyshev orthogonal polynomial is used as functional expansion for solution of higher dimension problems. Four benchmarked datasets for classification are collected from UCI repository. The computational results are compared with MLP trained by different training algorithms namely, Gradient Descent back propagation (MLP-GD), Levenberg Marquardt back propagation (MLP-LM), Gradient Descent back propagation with momentum (MLP-GDM), and Gradient Descent with momentum and adaptive learning rate (MLP-GDX). The findings show that, proposed model outperforms all compared methods in terms of accuracy, precision and sensitivity.