Functional Expansions Based Multilayer Perceptron Neural Network for Classification Task

Authors

  • Umer Iqbal University Tun Hussein Onn Malaysia, Faculty of Computer Science and Information Technology, Johor; University, Faisalabad Campus, Pakistan Riphah College of Computing Riphah International
  • Rozaida Ghazali University Tun Hussein Onn Malaysia, Faculty of Computer Science and Information Technology, Johor
  • Muhammad Faheem Mushtaq University Tun Hussein Onn Malaysia, Faculty of Computer Science and Information Technology, Johor
  • Afshan Kanwal University Tun Hussein Onn Malaysia, Department of Mathematics and Statistics, Johor

DOI:

https://doi.org/10.13053/cys-22-4-2602

Keywords:

Data mining, classification, shifted Genocchi polynomials, Chebyshev wavelets, multilayer perceptron

Abstract

Artificial neural network has been proved among the best tools in data mining for classification tasks. Where, Multilayer Perceptron (MLP) is known as benchmarked technique for classification tasks due to common use and easy implementation. Meanwhile, it is fail to make high combination of inputs from lower feature space to higher feature space. In this paper, Shifted Genocchi polynomials and Chebyshev Wavelets functional expansions based Multilayer Perceptron techniques with Levenberg Marquardt back propagation learning are proposed to deal with high dimension problems in classification tasks. Five datasets from UCI repository and KEEL datasets were collected to evaluate the performance in terms of five evaluation measures. T-test was applied to check the significance of the proposed techniques. The comparison results show that the proposed models outperform in terms of accuracy, sensitivity, specificity, precision and f-measure.

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Published

2018-12-31

Issue

Vol. 22 No. 4 (2018): Topic Trends in Computing Research (Guest Editors: A. Aguilar-Meléndez, E. Moya-Sánchez)

Section

Articles

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