Skip to main content
SpringerLink
Log in

A Survey on Extreme Learning Machine and Evolution of Its Variants

  • Conference paper
  • First Online:
Book cover Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1035))

Abstract

Extreme Learning Machine (ELM) is most popular emerging learning algorithm that modify classical ‘Generalized’ single hidden layer feed forward network. Though some traditional gradient based learning algorithm like variant Levenberg-Marguardt (LM) and Back propagation (BP) are widely utilized for training in multi layer FFNN but some drawbacks of this mechanism are the most prime issue to promote ELM. It imparts efficient learning solutions for different practices of classification and regression under supervise learning. ELM sharply deals with the messes arise from the gradient based learning algorithm like stopping criteria, learning rate, learning epoch, local minimum etc. But due to some fallibility the different concepts of variants of ELM are presented. This paper clarifies about Extreme Learning Machine along with different types of variants.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990. IEEE, July 2004

    Google Scholar 

  2. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)

    Article  Google Scholar 

  3. Rong, H.J., Ong, Y.S., Tan, A.H., Zhu, Z.: A fast pruned-extreme learning machine for classification problem. Neurocomputing 72(1–3), 359–366 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. 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 

  6. Zhao, J., Wang, Z., Cao, F.: Extreme learning machine with errors in variables. World Wide Web 17(5), 1205–1216 (2014)

    Article  Google Scholar 

  7. Zhou, Z.H., Zhao, J.W., Cao, F.L.: Surface reconstruction based on extreme learning machine. Neural Comput. Appl. 23(2), 283–292 (2013)

    Article  Google Scholar 

  8. 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 

  9. Huang, G.B., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16–18), 3460–3468 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Feng, G., Huang, G.B., Lin, Q., Gay, R.K.L.: Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans. Neural Netw. 20(8), 1352–1357 (2009)

    Article  Google Scholar 

  12. Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., Lendasse, A.: OP-ELM: optimally pruned extreme learning machine. IEEE Trans. Neural Netw. 21(1), 158–162 (2010)

    Article  Google Scholar 

  13. Lan, Y., Soh, Y.C., Huang, G.B.: Two-stage extreme learning machine for regression. Neurocomputing 73(16–18), 3028–3038 (2010)

    Article  Google Scholar 

  14. Cao, J., Lin, Z., Huang, G.B., Liu, N.: Voting based extreme learning machine. Inf. Sci. 185(1), 66–77 (2012)

    Article  MathSciNet  Google Scholar 

  15. Shukla, S., Wadhvani, R., Bharti, J., Gyanchandani, M.: Voting based extreme learning machine with search based ensemble pruning. IJCSNS 17(3), 223 (2017)

    Google Scholar 

  16. Liang, N.Y., Huang, G.B., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17(6), 1411–1423 (2006)

    Article  Google Scholar 

  17. Lan, Y., Soh, Y.C., Huang, G.B.: Ensemble of online sequential extreme learning machine. Neurocomputing 72(13–15), 3391–3395 (2009)

    Article  Google Scholar 

  18. Zhao, J., Wang, Z., Park, D.S.: Online sequential extreme learning machine with forgetting mechanism. Neurocomputing 87, 79–89 (2012)

    Article  Google Scholar 

  19. Zhu, Q.Y., Qin, A.K., Suganthan, P.N., Huang, G.B.: Evolutionary extreme learning machine. Pattern Recogn. 38(10), 1759–1763 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Li, M.B., Huang, G.B., Saratchandran, P., Sundararajan, N.: Fully complex extreme learning machine. Neurocomputing 68, 306–314 (2005)

    Article  Google Scholar 

  22. Deng, W.Y., Zheng, Q.H., Lian, S., Chen, L., Wang, X.: Ordinal extreme learning machine. Neurocomputing 74(1–3), 447–456 (2010)

    Article  Google Scholar 

  23. Liu, X., Li, P., Gao, C.: Symmetric extreme learning machine. Neural Comput. Appl. 22(3–4), 551–558 (2013)

    Article  Google Scholar 

  24. Zhang, W.B., Ji, H.B.: Fuzzy extreme learning machine for classification. Electron. Lett. 49(7), 448–450 (2013)

    Article  Google Scholar 

  25. Castano, A., Fernandez-Navarro, F., Hervás-Martínez, C.: PCA-ELM: a robust and pruned extreme learning machine approach based on principal component analysis. Neural Process. Lett. 37(3), 377–392 (2013)

    Article  Google Scholar 

  26. He, Q., Shang, T., Zhuang, F., Shi, Z.: Parallel extreme learning machine for regression based on MapReduce. Neurocomputing 102, 52–58 (2013)

    Article  Google Scholar 

  27. Horata, P., Chiewchanwattana, S., Sunat, K.: Robust extreme learning machine. Neurocomputing 102, 31–44 (2013)

    Article  Google Scholar 

  28. Yu, Q., Miche, Y., Eirola, E., Van Heeswijk, M., SéVerin, E., Lendasse, A.: Regularized extreme learning machine for regression with missing data. Neurocomputing 102, 45–51 (2013)

    Article  Google Scholar 

  29. Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101, 229–242 (2013)

    Article  Google Scholar 

  30. Wang, B., Wang, G., Li, J., Wang, B.: Update strategy based on region classification using ELM for mobile object index. Soft Comput. 16(9), 1607–1615 (2012)

    Article  Google Scholar 

  31. Zheng, W., Qian, Y., Lu, H.: Text categorization based on regularization extreme learning machine. Neural Comput. Appl. 22(3–4), 447–456 (2013)

    Article  Google Scholar 

  32. Li, G., Niu, P.: An enhanced extreme learning machine based on ridge regression for regression. Neural Comput. Appl. 22(3–4), 803–810 (2013)

    Article  Google Scholar 

  33. Balasundaram, S.: On extreme learning machine for E-insensitive regression in the primal by Newton method. Neural Comput. Appl. 22(3–4), 559–567 (2013)

    Article  Google Scholar 

  34. Zong, W., Huang, G.B.: Face recognition based on extreme learning machine. Neurocomputing 74(16), 2541–2551 (2011)

    Article  Google Scholar 

  35. Mohammed, A.A., Minhas, R., Wu, Q.J., Sid-Ahmed, M.A.: Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recogn. 44(10–11), 2588–2597 (2011)

    Article  Google Scholar 

  36. Nian, R., He, B., Lendasse, A.: 3D object recognition based on a geometrical topology model and extreme learning machine. Neural Comput. Appl. 22(3–4), 427–433 (2013)

    Article  Google Scholar 

  37. Yang, J., Jiao, Y., Xiong, N., Park, D.: Fast face gender recognition by using local ternary pattern and extreme learning machine. KSII Trans. Internet Inf. Syst. (TIIS) 7(7), 1705–1720 (2013)

    Article  Google Scholar 

  38. Yang, J., Xie, S., Yoon, S., Park, D., Fang, Z., Yang, S.: Fingerprint matching based on extreme learning machine. Neural Comput. Appl. 22(3–4), 435–445 (2013)

    Article  Google Scholar 

  39. Chen, F.L., Ou, T.Y.: Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Expert Syst. Appl. 38(3), 1336–1345 (2011)

    Article  Google Scholar 

  40. Xu, Y., Dai, Y., Dong, Z.Y., Zhang, R., Meng, K.: Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems. Neural Comput. Appl. 22(3–4), 501–508 (2013)

    Article  Google Scholar 

  41. Malathi, V., Marimuthu, N.S., Baskar, S., Ramar, K.: Application of extreme learning machine for series compensated transmission line protection. Eng. Appl. Artif. Intell. 24(5), 880–887 (2011)

    Article  Google Scholar 

  42. Obaidullah, S.M., Halder, C., Santosh, K.C., Das, N., Roy, K.: PHDIndic\(\_\)11: page-level handwritten document image dataset of 11 official Indic scripts for script identification. Multimedia Tools Appl. 77(2), 1643–1678 (2018)

    Article  Google Scholar 

  43. Mukherjee, H., Obaidullah, S.M., Santosh, K.C., Phadikar, S., Roy, K.: Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal. Int. J. Speech Technol. 21, 753–760 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Aliah University, Kolkata, India

    Subhasmita Ghosh & Sk Md. Obaidullah

  2. Department of Computer Science, West Bengal State University, Kolkata, India

    Himadri Mukherjee & Kaushik Roy

  3. Department of Computer Science, The University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  4. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Nibaran Das

Authors
  1. Subhasmita Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Himadri Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sk Md. Obaidullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. C. Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nibaran Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kaushik Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Himadri Mukherjee .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghosh, S., Mukherjee, H., Obaidullah, S.M., Santosh, K.C., Das, N., Roy, K. (2019). A Survey on Extreme Learning Machine and Evolution of Its Variants. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_50

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9181-1_50

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9180-4

  • Online ISBN: 978-981-13-9181-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation