Skip to main content
SpringerLink
Log in

Improving Functional Link Neural Network Learning Scheme for Mammographic Classification

  • Conference paper
  • First Online:
Advances in Neural Networks (WIRN 2015)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 54))

Included in the following conference series:

Abstract

Functional Link Neural Network (FLNN) has become as an important tool used in classification tasks due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network. Since FLNN uses Backpropagation algorithm as the standard learning scheme, the method however prone to get trapped in local minima which affect its classification performance. This paper proposed the implementation of modified Bee-Firefly algorithm as an alternative learning scheme for FLNN for the task of mammographic mass classification. The implementation of the proposed learning scheme demonstrated that the FLNN can successfully perform the classification task with better accuracy result on unseen data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Giles, C.L., Maxwell, T.: Learning, invariance, and generalization in high-order neural networks. Appl. Opt. 26(23), 4972–4978 (1987)

    Article  Google Scholar 

  2. Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5), 76–79 (1992)

    Article  Google Scholar 

  3. Misra, B.B., Dehuri, S.: Functional link artificial neural network for classification task in data mining. J. Comput. Sci. 3(12), 948–955 (2007)

    Article  Google Scholar 

  4. Dehuri, S., Cho, S.-B.: A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN. Neural Comput. Appl. 19(2), 187–205 (2010)

    Article  Google Scholar 

  5. Hassim, Y.M.M., Ghazali, R.: A modified artificial bee colony optimization for functional link neural network training. In: Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013), pp. 69–78. Springer, Singapore (2014)

    Google Scholar 

  6. Haring, S., Kok, J.: Finding functional links for neural networks by evolutionary computation. In: Van de Merckt T et al. (ed.) Proceedings of the Fifth Belgian–Dutch Conference on Machine Learning, BENELEARN1995, pp. 71–78. Brussels, Belgium (1995)

    Google Scholar 

  7. Haring, S., Kok, J., Van Wesel, M.: Feature selection for neural networks through functional links found by evolutionary computation. In: ILiu X et al. (ed.) Advances in Intelligent Data Analysis (IDA-97), LNCS 1280, pp. 199–210 (1997)

    Google Scholar 

  8. Abu-Mahfouz, I.-A.: A comparative study of three artificial neural networks for the detection and classification of gear faults. Int. J. Gen. Syst. 34(3), 261–277 (2005)

    Google Scholar 

  9. Sierra, A., Macias, J.A., Corbacho, F.: Evolution of functional link networks. IEEE Trans. Evol. Comput. 5(1), 54–65 (2001)

    Article  Google Scholar 

  10. Dehuri, S., Mishra, B.B., Cho, S.-B.: Genetic feature selection for optimal functional link artificial neural network in classification. In: Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning. pp. 156–163. Springer, Daejeon, South Korea (2008)

    Google Scholar 

  11. Hassim, Y.M.M., Ghazali, R.: Optimizing functional link neural network learning using modified bee colony on multi-class classifications. In: Advanced in Computer Science and its Applications, pp. 153–159. Springer Berlin Heidelberg. (2014)

    Google Scholar 

  12. Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University, Engineering Faculty, Computer Science Department, Kayseri/Turkiye (2005)

    Google Scholar 

  13. Yang, X.S.: Firefly algorithm. Eng. Optim. 221–230 (2010)

    Google Scholar 

  14. Guo, L., Wang, G.-G., Wang, H., Wang, D.: An effective hybrid firefly algorithm with harmony search for global numerical optimization. Sci. World J. 2013, 9 (2013)

    Google Scholar 

  15. Bacanin, N., Tuba, M.: Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint. Sci. World J. 2014, 721521 (2014)

    Article  Google Scholar 

  16. Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  17. Chou, S.-M., Lee, T.-S., Shao, Y.E., Chen, I.F.: Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Syst. Appl. 27(1), 133–142 (2004)

    Article  Google Scholar 

  18. Frank, A., Asuncion, A.: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Irvine, CA, University of California, School of Information and Computer Science (2010)

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 86400, Batu Pahat, Johor, Malaysia

    Yana Mazwin Mohmad Hassim & Rozaida Ghazali

Authors
  1. Yana Mazwin Mohmad Hassim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rozaida Ghazali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yana Mazwin Mohmad Hassim .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Hassim, Y.M.M., Ghazali, R. (2016). Improving Functional Link Neural Network Learning Scheme for Mammographic Classification. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33747-0_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33746-3

  • Online ISBN: 978-3-319-33747-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation