Fibonacci Polynomials Based Functional Link Neural Network for Classification Tasks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

The artificial neural network has been proved among the best tools in data mining for classification tasks. The concept of obtaining more accurate classifier with less computational complexity has been gaining importance, because of day by day increase in the data. Several numbers of models have been developed for classification problems. This paper is the depiction of higher order neural networks especially Fibonacci Functional Link Neural Network (FFLNN) for data classification. The coefficients of individual terms in Fibonacci polynomials are smaller than those of individual terms in the classical orthogonal polynomials. Additionally, less number of terms make it a preferable classifier regarding functional expansion. These properties lead this FFLNN to produce more accurate higher order neural network with less computational complexity to tackle the classification problems. Four datasets were collected from KEEL and LIBSVM dataset repositories. Computational results were compared with three benchmarked models including Chebyshev Functional Link Neural Network (CFLNN), Chebyshev Multilayer Perceptron (CMLP) and Multilayer Perceptron Neural Network (MLP). A t-test was applied to check the significance of the proposed classifier based on classification performance. The findings showed that the proposed classifier outperformed all benchmarked models in all evaluation measures.

Keywords

Data mining Classification Fibonacci polynomials Functional link neural network 

Notes

Acknowledgements

The authors would like to thank King Khalid University to provide the International Research Grant with Grant number A134 for supporting this research.

References

  1. 1.
    Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 30(4), 451–462 (2000)Google Scholar
  2. 2.
    Misra, B.B., Dehuri, S.: Functional Link Artificial Neural Network for Classification Task in Data Mining, vol. 1 (2007)Google Scholar
  3. 3.
    Chen, C., Duan, S., Cai, T., Liu, B.: Online 24-h solar power forecasting based on weather type classification using artificial neural network. Sol. Energy 85(11), 2856–2870 (2011)CrossRefGoogle Scholar
  4. 4.
    Al-Jarrah, O., Arafat, A.: Network intrusion detection system using neural network classification of attack behavior. J. Adv. Inf. Technol. 6(1) (2015)Google Scholar
  5. 5.
    Mason, M.: Classification of Handwritten Digits Using an Artificial Neural Network (2015)Google Scholar
  6. 6.
    Babaei, T., Lim, C.P., Abdi, H., Nahavandi, S.: A Modified functional link neural network for data classification. In: Emerging Trends in Neuro Engineering and Neural Computation, pp. 229–244. Springer, Singapore (2017)Google Scholar
  7. 7.
    Iqbal, U., Ghazali, R.: Chebyshev multilayer perceptron neural network with Levenberg Marquardt-back propagation learning for classification tasks. In: International Conference on Soft Computing and Data Mining, pp. 162–170. Springer, Cham (2016)Google Scholar
  8. 8.
    Mohmad Hassim, Y.M., Ghazali, R.: Using artificial bee colony to improve functional link neural network training. In: Applied Mechanics and Materials, pp. 2102–2108. Trans Tech Publications (2013)Google Scholar
  9. 9.
    Ghazali, R., Husaini, N.A., Ismail, L.H., Herawan, T., Hassim, Y.Y.M.: The performance of a Recurrent HONN for temperature time series prediction. In: 2014 International Joint Conference on Neural Network, pp. 518–524 (2014)Google Scholar
  10. 10.
    Ghazali, R., Hussain, A.J., Merabti, M.: Higher order neural networks and their applications to financial time series prediction. In: Artificial Intelligence and Soft Computing, pp. 120–125 (2006)Google Scholar
  11. 11.
    Kumar, M., Singh, S., Rath, S.K.: Classification of microarray data using functional link neural network. Proc. Comput. Sci. 57, 727–737 (2015)CrossRefGoogle Scholar
  12. 12.
    Paliwal, M., Kumar, U.A.: Neural networks and statistical techniques: a review of applications. Expert Syst. App. 36(1), 2–17 (2009)CrossRefGoogle Scholar
  13. 13.
    Silva-Ramírez, E.L., Pino-Mejías, R., López-Coello, M.: Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns. Appl. Soft Comput. 29, 65–74 (2015)CrossRefGoogle Scholar
  14. 14.
    Mabu, S., Obayashi, M., Kuremoto, T.: Ensemble learning of rule-based evolutionary algorithm using multi-layer perceptron for supporting decisions in stock trading problems. Appl. Soft Comput. 36, 357–367 (2015)CrossRefGoogle Scholar
  15. 15.
    Jedliński, Ł., Jonak, J.: Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform. Appl. Soft Comput. 30, 636–641 (2015)CrossRefGoogle Scholar
  16. 16.
    Bebarta, D.K., Rout, A.K., Biswal, B., Dash, P.K.: Forecasting and classification of Indian stocks using different polynomial functional link artificial neural networks. In: 2012 Annual IEEE India Conference (INDICON), pp. 178–182 (2012)Google Scholar
  17. 17.
    Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5), 76–79 (1992)CrossRefGoogle Scholar
  18. 18.
    Li, M., Liu, J., Jiang, Y., Feng, W.: Complex-Chebyshev functional link neural network behavioral model for broadband wireless power amplifiers. IEEE Trans. Microw. Theory Tech. 60(6), 1979–1989 (2012)CrossRefGoogle Scholar
  19. 19.
    Ozdemir, G., Simsek, Y.: Generating functions for two-variable polynomials related to a family of Fibonacci type polynomials and numbers. Filomat 30(4), 969–975 (2016)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaParit Raja, Batu PahatMalaysia
  2. 2.Department of Computer ScienceCollege of Computer Science, King Khalid UniversityAbhaSaudi Arabia

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