Advertisement

Optimizing Functional Link Neural Network Learning Using Modified Bee Colony on Multi-class Classifications

  • Yana Mazwin Mohmad Hassim
  • Rozaida Ghazali
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 279)

Abstract

Functional Link Neural Network (FLNN) has emerged as an important tool for solving classification problems and widely applied in many engineering and scientific problems. FLNN is known to be conveniently used as compared to ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights. The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. However, BP-learning algorithm has difficulties such as trapping in local minima and slow convergence especially for solving non-linearly separable classification problems. In this work, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. With modifications on the employed bee’s exploitation phase, the implementation of the mABC as a learning scheme for FLNN is expected to give a better accuracy result for the classification tasks.

Keywords

Classification Functional Link Neural Network Artificial Bee Colony Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Misra, B.B., Dehuri, S.: Functional Link Artificial Neural Network for Classification Task in Data Mining. Journal of Computer Science 3(12), 948–955 (2007)CrossRefGoogle Scholar
  2. 2.
    Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5), 76–79 (1992)CrossRefGoogle Scholar
  3. 3.
    Widrow, B., Rumelhart, D.E., Lehr, M.A.: Neural networks: applications in industry, business and science. Commun. ACM 37(3), 93–105 (1994)CrossRefGoogle Scholar
  4. 4.
    Dehuri, S., Cho, S.-B.: A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN. Neural Computing & Applications 19(2), 187–205 (2010)CrossRefGoogle Scholar
  5. 5.
    Karaboga, D., Akay, B., Ozturk, C.: Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 318–329. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Erciyes University, Engineering Faculty, Computer Science Department, Kayseri/Turkiye (2005)Google Scholar
  7. 7.
    Pao, Y.H.: Adaptive pattern recognition and neural networks (1989)Google Scholar
  8. 8.
    Abu-Mahfouz, I.-A.: A comparative study of three artificial neural networks for the detection and classification of gear faults. International Journal of General Systems 34(3), 261–277 (2005)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Liu, L.M., Manry, M.T., Amar, F., Dawson, M.S., Fung, A.K.: Image classification in remote sensing using functional link neural networks. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (1994)Google Scholar
  10. 10.
    Dehuri, S., Cho, S.-B.: Evolutionarily optimized features in functional link neural network for classification. Expert Systems with Applications 37(6), 4379–4391 (2010)CrossRefGoogle Scholar
  11. 11.
    Klaseen, M., Pao, Y.H.: The functional link net in structural pattern recognition. In: 1990 IEEE Region 10 Conference on Computer and Communication Systems, TENCON 1990 (1990)Google Scholar
  12. 12.
    Park, G.H., Pao, Y.H.: Unconstrained word-based approach for off-line script recognition using density-based random-vector functional-link net. Neurocomputing 31(1-4), 45–65 (2000)CrossRefGoogle Scholar
  13. 13.
    Majhi, R., Panda, G., Sahoo, G.: Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Systems with Applications 36(3, Part 2), 6800–6808 (2009)Google Scholar
  14. 14.
    Ghazali, R., Hussain, A.J., Liatsis, P.: Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals. Expert Systems with Applications 38(4), 3765–3776 (2011)CrossRefGoogle Scholar
  15. 15.
    Dehuri, S., Mishra, B.B., Cho, S.-B.: Genetic Feature Selection for Optimal Functional Link Artificial Neural Network in Classification. In: Fyfe, C., Kim, D., Lee, S.-Y., Yin, H. (eds.) IDEAL 2008. LNCS, vol. 5326, pp. 156–163. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Mohmad Hassim, Y.M., Ghazali, R.: Using Artificial Bee Colony to Improve Functional Link Neural Network Training. Applied Mechanics and Materials 263, 2102–2108 (2013)Google Scholar
  17. 17.
    Mohmad Hassim, Y.M., Ghazali, R.: Training a Functional Link Neural Network Using an Artificial Bee Colony for Solving a Classification Problems. Journal of Computing Press 4(9), 110–115 (2012)Google Scholar
  18. 18.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository, School of Information and Computer Science, Irvine, CA. University of California (2010), http://archive.ics.uci.edu/ml

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaJohorMalaysia

Personalised recommendations