Skip to main content
SpringerLink
Log in

Extreme Learning Machine for Multi-label Classification

  • Conference paper
  • First Online:
Proceedings of ELM 2018 (ELM 2018)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 11))

Included in the following conference series:

  • 385 Accesses

Abstract

Extreme Learning Machine (ELM) has drawn more and more attention in the machine learning fields because of its fast training speed and good generalization ability. In this paper, we employ ELM algorithm to deal with multi-label classification problems. The essence of the proposed algorithm is to convert the multi-label classification problem into some single-label classifications, and fully considers the relationship among different labels. The hidden nodes of ELM algorithm for one label classification come from three parts. Some of them are initialized nodes after PCA dimensionality reduction. And we design a backup pool to select appropriate hidden nodes. In addition, the nodes of the previous label classification would flow into the classification of the current label. In the simulation part, three famous databases demonstrate the satisfied classification accuracy of the proposed method.

Supported by organization the National Natural Science Foundation of China (No. 61806208, 61502498), Fundamental Research Funds for the Central Universities (No. 3122018S008), Tianjin Education Committee Research Project (No. 2018KJ246).

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. Tsoumakas, G., Katakis, K.: Multi-label classification: an overview. Int. J. Data Warehouse. Min. 3(3), 1–13 (2006)

    Google Scholar 

  2. Luo, F., Guo, W., Yu, Y., Chen, G.: A multi-label classification algorithm based on kernel extreme learning machine. Neurocomputing 260, 313–320 (2017)

    Article  Google Scholar 

  3. Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)

    Article  Google Scholar 

  4. Clare, A., King, R.: Knowledge discovery in multi-label phenotype data. Principles of data mining and knowledge discovery, pp. 42–53 (2001)

    Chapter  Google Scholar 

  5. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of online learning and an application to boosting. IN: European Conference on Computational Learning Theory, pp. 23–37. Springer (1995)

    Google Scholar 

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

    Article  Google Scholar 

  7. Huang, G.B.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 6(3), 376–390 (2014)

    Article  Google Scholar 

  8. 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 

  9. Zhang, H.G., Yin, Y.X., Zhang, S.: An improved elm algorithm for the measurement of hot metal temperature in blast furnace. Neurocomputing 174, 232–237 (2016)

    Article  Google Scholar 

  10. Cao, J.W., Zhang, K., Yong, H., Lai, X., Chen, B., Lin, Z.: Extreme learning machine with affine transformation inputs in an activation function. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–15 (2018). https://doi.org/10.1109/TNNLS.2018.2877468

    Article  Google Scholar 

  11. Sun, X., Xu, J., Jiang, C., Feng, J., Chen, S.S., He, F.: Extreme learning machine for multi-label classification. Entropy 18(6), 225 (2016). https://doi.org/10.3390/e18060225

    Article  MathSciNet  Google Scholar 

  12. Cao, J., Zhang, K., Luo, M., Yin, C., Lai, X.: Extreme learning machine and adaptive sparse representation for image classification. Neural Netw. 81, 91–102 (2016)

    Article  Google Scholar 

  13. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. lab. Syst. 2(1–3), 37–52 (1987)

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin, 300300, China

    Haigang Zhang, Jinfeng Yang & Guimin Jia

  2. Basic Experimental Center, Civil Aviation University of China, Tianjin, 300300, China

    Shaocheng Han

Authors
  1. Haigang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinfeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guimin Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaocheng Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinfeng Yang .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Xiasha, Hangzhou, China

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Taipa, Macao

    Chi Man Vong

  3. Nokia Bell Labs, Cybersecurity Research, Espoo, Finland

    Yoan Miche

  4. Department of Information and Logistics Technology, University of Houston, Houston, TX, USA

    Amaury Lendasse

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, H., Yang, J., Jia, G., Han, S. (2020). Extreme Learning Machine for Multi-label Classification. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_19

Download citation

Publish with us

Policies and ethics

Search

Navigation