Skip to main content
SpringerLink
Log in

Functional Link Artificial Neural Network for Multi-label Classification

  • Conference paper
  • First Online:
Mining Intelligence and Knowledge Exploration (MIKE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

In this article, a multi-label functional link artificial neural network (MLFLANN) has been developed to efficiently perform multi-label data classification. The input data is functionally expanded to a higher dimension, followed by iterative learning of the multi-label FLANN (MLFLANN) using the training set. The architecture of the network is less complex and the input space dimension is improved in an attempt to overcome the non-linear nature of the multi-label classification problem. The method has been validated on various multi-label datasets and the results are found to be encouraging.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)

    Article  Google Scholar 

  2. Cheng, W., Hüllermeier, E., Dembczynski, K.J.: Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of the 27th International Conference on Machine Learning, ICML 2010, pp. 279–286 (2010)

    Google Scholar 

  3. Cover, T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electron. Comput. 3, 326–334 (1965)

    Article  MATH  Google Scholar 

  4. De Comité, F., Gilleron, R., Tommasi, M.: Learning multi-label alternating decision trees from texts and data. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 35–49. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45065-3_4

    Chapter  Google Scholar 

  5. Dehuri, S., Cho, S.B.: Evolutionarily optimized features in functional link neural network for classification. Expert Syst. Appl. 37(6), 4379–4391 (2010)

    Article  Google Scholar 

  6. Dehuri, S., Cho, S.B.: A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. Neural Comput. Appl. 19(2), 317–328 (2010)

    Article  Google Scholar 

  7. Dehuri, S., Roy, R., Cho, S.B., Ghosh, A.: An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J. Syst. Softw. 85(6), 1333–1345 (2012)

    Article  Google Scholar 

  8. Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems, pp. 681–687 (2002)

    Google Scholar 

  9. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice - Hall of India, New Delhi (2008)

    MATH  Google Scholar 

  10. Herrera, F., Charte, F., Rivera, A.J., Del Jesus, M.J.: Multilabel Classification: Problem Analysis, Metrics and Techniques. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41111-8

    Book  Google Scholar 

  11. Kongsorot, Y., Horata, P.: Multi-label classification with extreme learning machine. In: 6th International Conference on Knowledge and Smart Technology (KST), pp. 81–86. IEEE (2014)

    Google Scholar 

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

    Article  Google Scholar 

  13. Pao, Y.-H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Boston (1989)

    MATH  Google Scholar 

  14. Pao, Y.-H., Phillips, S.M., Sobajic, D.J.: Neural-net computing and the intelligent control of systems. Int. J. Control 56(2), 263–289 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  15. Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.P.: Multi-label classification of music into emotions. In: Ninth International Conference on Music Information Retrieval (ISMIR), vol. 8, pp. 325–330 (2008)

    Google Scholar 

  16. Turnbull, D., Barrington, L., Torres, D., Lanckriet, G.: Semantic annotation and retrieval of music and sound effects. IEEE Trans. Audio Speech Lang. Process. 16(2), 467–476 (2008)

    Article  Google Scholar 

  17. Zhang, M.L.: ML-RBF: RBF neural networks for multi-label learning. Neural Process. Lett. 29(2), 61–74 (2009)

    Article  MathSciNet  Google Scholar 

  18. Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338–1351 (2006)

    Article  Google Scholar 

  19. Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, India

    Anwesha Law & Ashish Ghosh

  2. Department of Computer Science, Vidyasagar University, Medinipur, 721102, India

    Konika Chakraborty

Authors
  1. Anwesha Law
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Konika Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashish Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish Ghosh .

Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Rajarshi Pal

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Law, A., Chakraborty, K., Ghosh, A. (2017). Functional Link Artificial Neural Network for Multi-label Classification. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-71928-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71927-6

  • Online ISBN: 978-3-319-71928-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation