Abstract
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.
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Acknowledgments
The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of High Education (MOHE) for financially supporting this research under Fundamental Research Grant Scheme (FRGS), Vote No 1235.
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Iqbal, U., Ghazali, R. (2017). Chebyshev Multilayer Perceptron Neural Network with Levenberg Marquardt-Back Propagation Learning for Classification Tasks. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_17
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DOI: https://doi.org/10.1007/978-3-319-51281-5_17
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