Skip to main content
SpringerLink
Log in

An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm

  • Chapter
  • First Online:
Nature-Inspired Computation in Data Mining and Machine Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 855))

Abstract

The second generation of algorithms intended for neural networks is named extreme learning machines (ELMs). Since the computing of output weights of ELM encounters the outliers problems, we advise a recently introduced Flower Pollination Algorithm (FPA) for accurately tuning synaptic input weights of ELM. The hybridization between ELM and FPA provides robust FPA-ELM approach which can efficiently solve outlier problems as well as it can significantly reduce the size of latent nodes. Extensive simulation results based on 16 well-known benchmark problems were conducted to reveal the effectiveness of the stated hybridization. Furthermore, it has been proved that our FPA-ELM approach is superior to other state-of-the-art algorithms from literature and that it can learn much faster weight coefficients compared to the other traditional learning methods, as well.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Alihodzic, A., Tuba, E., Tuba, M.: An upgraded bat algorithm for tuning extreme learning machines for data classification. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’17, pp. 125–126. ACM, New York (2017). https://doi.org/10.1145/3067695.3076088

  2. Alihodzic, A.: Training feed-forward neural networks employing improved bat algorithm for digital image compression. In: Lirkov, I., Margenov, S. (eds.) Large-Scale Scientific Computing, pp. 315–323. Springer International Publishing, Cham (2018)

    Chapter  Google Scholar 

  3. Ben-Israel, A., Greville, T.N.E.: Generalized inverses: theory and applications. In: CMS Books in Mathematics, Springer edn (2003)

    Google Scholar 

  4. Cao, J., Lin, Z., Huang, G.B.: Self-adaptive evolutionary extreme learning machine. Neural Process. Lett. 36(3), 285–305 (2012)

    Article  Google Scholar 

  5. Chen, Z., Ni, C.: Neural network approaches for text document categorization. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, pp. 1054–1060. IEEE (2006). https://doi.org/10.1109/IJCNN.2006.246805

  6. Chittka, L., Thomson, J.D., Waser, N.M.: Flower constancy, insect psychology, and plant evolution. Naturwissenschaften 86(8), 361–377 (1999). https://doi.org/10.1007/s001140050636

    Article  Google Scholar 

  7. Courrieu, P.: Straight monotonic embedding of data sets in euclidean spaces. Neural Netw. 15(10), 1185–1196 (2002). https://doi.org/10.1016/S0893-6080(02)00091-6

    Article  Google Scholar 

  8. Delve repository. http://www.cs.toronto.edu/%7Edelve/data/datasets.html

  9. Ding, S., Zhao, H., Zhang, Y., Xu, X., Nie, R.: Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 44(1), 103–115 (2015). https://doi.org/10.1007/s10462-013-9405-z

    Article  Google Scholar 

  10. Du, K.L.: Clustering: a neural network approach. Neural Netw. 23(1), 89–107 (2010)

    Article  Google Scholar 

  11. Feng, G., Qian, Z., Zhang, X.: Evolutionary selection extreme learning machine optimization for regression. Soft Comput. 16(9), 1485–1491 (2012)

    Article  Google Scholar 

  12. Frank, A., Asuncion, A.: Uci Machine Learning Repository (2010). http://archive.ics.uci.edu/ml/index.php

  13. Han, F., Huang, D.S.: Improved extreme learning machine for function approximation by encoding a priori information. Neurocomputing 69(16–18), 2369–2373 (2006)

    Article  Google Scholar 

  14. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, New York (2008)

    Google Scholar 

  15. Huang, G.B.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 6(3), 376–390 (2014). https://doi.org/10.1007/s12559-014-9255-2

    Article  Google Scholar 

  16. Huang, G.B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70(16–18), 3056–3062 (2007)

    Article  Google Scholar 

  17. Huang, G.B., Chen, Y.Q., Babri, H.A.: Classification ability of single hidden layer feedforward neural networks. IEEE Trans. Neural Netw. 11(3), 799–801 (2000)

    Article  Google Scholar 

  18. Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879–892 (2006)

    Article  Google Scholar 

  19. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)

    Article  Google Scholar 

  20. Huang, G.B., Ding, X., Zhou, H.: Optimization method based extrem elearning machine for classification. Neurocomputing 74(1–3), 155–163 (2010)

    Article  Google Scholar 

  21. Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 513–529 (2012)

    Article  Google Scholar 

  22. Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015). https://doi.org/10.1016/j.neunet.2014.10.001

    Article  MATH  Google Scholar 

  23. Kasun, L.L.C., Yang, Y., Huang, G., Zhang, Z.: Dimension reduction with extreme learning machine. IEEE Trans. Image Process. 25(8), 3906–3918 (2016). https://doi.org/10.1109/TIP.2016.2570569

    Article  MathSciNet  MATH  Google Scholar 

  24. Kim, T.H.: Pattern recognition using artificial neural network: a review. In: Information Security and Assurance Communications in Computer and Information Science, vol. 76, pp. 138–148. Springer International Publishing (2010)

    Google Scholar 

  25. Kourehli, S.S.: Application of extreme learning machine to damage detection of plate-like structures. Int. J. Struct. Stab. Dyn. 17(7), 19 (2017). https://doi.org/10.1142/S0219455417500687

    Article  MathSciNet  Google Scholar 

  26. Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S.: Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. Neural Netw. 14(1), 79–88 (2003). https://doi.org/10.1109/TNN.2002.804317

    Article  Google Scholar 

  27. Li, G., Niu, P., Ma, Y., Wang, H., Zhang, W.: Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency. Knowledge-Based Syst. 67, 278–289 (2014). https://doi.org/10.1016/j.knosys.2014.04.042

    Article  Google Scholar 

  28. Lin, C.K.: H\(\infty \) reinforcement learning control of robot manipulators using fuzzy wavelet networks. Fuzzy Sets Syst. 160(12), 1765–1786 (2009). https://doi.org/10.1016/j.fss.2008.09.010

    Article  MathSciNet  MATH  Google Scholar 

  29. Mao, L., Zhang, L., Liu, X., Li, C., Yang, H.: Improved extreme learning machine and its application in image quality assessment. Math. Prob. Eng. (Article ID 426152), 7 (2014). https://doi.org/10.1155/2014/426152

    MathSciNet  MATH  Google Scholar 

  30. Matias, T., Souza, F., Araujo, R., Antunes, C.H.: Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine. Neurocomputing 129 (2014). https://doi.org/10.1016/j.neucom.2013.09.016

    Article  Google Scholar 

  31. Nizar, A.H., Dong, Z.Y., Wang, Y.: Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans. Power Syst. 23(3), 946–955 (2008)

    Article  Google Scholar 

  32. Pavlyukevich, I.: Lévy flights, non-local search and simulated annealing. J. Comput. Phys. 226(2), 1830–1844 (2007). https://doi.org/10.1016/j.jcp.2007.06.008. http://www.sciencedirect.com/science/article/pii/S002199910700263X

    Article  MathSciNet  Google Scholar 

  33. Rakha, M.A.I.: On the Moore-Penrose generalized inverse matrix. Appl. Math. Comput. 158(1), 185–200 (2004). https://doi.org/10.1016/j.amc.2003.09.004

    Article  MathSciNet  MATH  Google Scholar 

  34. Serre, D.: Matrices: Theory and Applications, Graduate Texts in Mathematics, vol. 216. Springer, Heidelberg (2010)

    Book  Google Scholar 

  35. Shi, X., Wang, J., Liu, G., Yang, L., Ge, X., Jiang, S.: Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs. J. Nat. Gas Sci. Eng. 33, 687–702 (2016). https://doi.org/10.1016/j.jngse.2016.05.060

    Article  Google Scholar 

  36. Shrivastava, G., Karmakar, S., Kowar, M.K., Guhathakurta, P.: Application of artificial neural networks in weather forecasting: a comprehensive literature review. Int. J. Comput. Appl. 51(18), 17–29 (2012)

    Article  Google Scholar 

  37. Stefanski, L., Boos, D.: Boos-stefanski variable selection. http://www4.stat.ncsu.edu/%7Eboos/var.select/

  38. Suresh, S., Radhakrishnan, V.B., Kim, H.J.: No-reference image quality assessment using modified extreme learning machine classifier. Appl. Soft Comput. 9(2), 541–552 (2009)

    Article  Google Scholar 

  39. Suresh, S., Saraswathi, S., Sundararajan, N.: Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng. Appl. Artif. Intell. 23(7), 1149–1157 (2010)

    Article  Google Scholar 

  40. Tan, P., Sa, W., Yu, L.: Applying extreme learning machine to classification of eeg bci. In: 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 228–232 (2016). https://doi.org/10.1109/CYBER.2016.7574827

  41. Tang, J., Deng, C., Huang, G.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2016). https://doi.org/10.1109/TNNLS.2015.2424995

    Article  MathSciNet  Google Scholar 

  42. Torgo, L.: Regression datasets. http://www.dcc.fc.up.pt/%7Eltorgo/Regression/DataSets.html

  43. Xiao, D., Li, B., Mao, Y.: A multiple hidden layers extreme learning machine method and its application. Math. Probl. Eng. (Article ID 4670187), 16 (2017). https://doi.org/10.1155/2017/4670187

    Google Scholar 

  44. Xue, J., Liu, Q., Li, M., Liu, X., Ye, Y., Wang, S., Yin, J.: Incremental multiple kernel extreme learning machine and its application in robo-advisors. Soft Comput. 22(11), 3507–3517 (2018). https://doi.org/10.1007/s00500-018-3031-2

    Article  Google Scholar 

  45. Xue, X., Yao, M., Wu, Z., Yang, J.: Genetic ensemble of extreme learning machine. Neurocomputing 129, 175–184 (2014)

    Article  Google Scholar 

  46. Yang, X.S.: Efficiency analysis of swarm intelligence and randomization techniques. J. Comput. Theoret. Nanosci. 9(2), 189–198 (2012)

    Article  Google Scholar 

  47. Yang, X.S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) Unconventional Computation and Natural Computation, pp. 240–249. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  48. Yang, H., Yi, J., Zhao, J., Dong, Z.Y.: Extreme learning machine based genetic algorithm and its application in power system economic dispatch. Neurocomputing 102, 154–162 (2013)

    Article  Google Scholar 

  49. Yeu, C.W.T., Lim, M.H., Huang, G.B., Agarwal, A., Ong, Y.S.: A new machine learning paradigm for terrain reconstruction. IEEE Geosci. Remote Sens. Lett. 3(3), 382–386 (2006)

    Article  Google Scholar 

  50. Yu, Y., Sun, Z.: Sparse coding extreme learning machine for classification. Neurocomputing 261, 50–56 (Advances in Extreme Learning Machines, ELM 2015) (2017). https://doi.org/10.1016/j.neucom.2016.06.078. http://www.sciencedirect.com/science/article/pii/S0925231217302072

    Article  Google Scholar 

  51. Zhao, G., Shen, Z., Miao, C., Man, Z.: On improving the conditioning of extreme learning machine: a linear case. In: Proceedings of the 7th IEEE International Conference on Information, Communications and Signal Processing, ICICS2009, pp. 1–5. IEEE (2009)

    Google Scholar 

  52. Zhaoa, J., Wanga, Z., Park, D.S.: Online sequential extreme learning machine with forgetting mechanism. Neurocomputing 87(15), 79–89 (2012)

    Article  Google Scholar 

  53. Zhu, Q.Y., Qin, K.A., Suganthan, P.N., Huang, G.B.: Evolutionary extreme learning machine. Pattern Recogn. 38(10), 1759–1763 (2005). https://doi.org/10.1016/j.patcog.2005.03.028

    Article  MATH  Google Scholar 

  54. Zong, W., Zhou, H., Huang, G.B., Lin, Z.: Face recognition based on kernelized extreme learning machine. Auton. Intell. Syst. Lect. Notes Comput. Sci. 6752, 263–272 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Adis Alihodzic

  2. Singidunum University, Belgrade, Serbia

    Eva Tuba

  3. State University of Novi Pazar, Novi Pazar, Serbia

    Milan Tuba

Authors
  1. Adis Alihodzic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eva Tuba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Milan Tuba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adis Alihodzic .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. College of Science, Xi’an Polytechnic University, Xi’an, China

    Xing-Shi He

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Alihodzic, A., Tuba, E., Tuba, M. (2020). An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm. In: Yang, XS., He, XS. (eds) Nature-Inspired Computation in Data Mining and Machine Learning. Studies in Computational Intelligence, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-030-28553-1_5

Download citation

Publish with us

Policies and ethics

Search

Navigation