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A High Speed Multi-label Classifier Based on Extreme Learning Machines

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Proceedings of ELM-2015 Volume 2

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 7))

Abstract

In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and discussed. Multi-label classification is a superset of traditional binary and multi-class classification problems. The proposed work extends the extreme learning machine technique to adapt to the multi-label problems. As opposed to the single-label problem, both the number of labels the sample belongs to, and each of those target labels are to be identified for multi-label classification resulting in increased complexity. The proposed high speed multi-label classifier is applied to six benchmark datasets comprising of different application areas such as multimedia, text and biology. The training time and testing time of the classifier are compared with those of the state-of-the-arts methods. Experimental studies show that for all the six datasets, our proposed technique have faster execution speed and better performance, thereby outperforming all the existing multi-label classification methods.

International Conference on Extreme Learning Machines, 2015.

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References

  1. Gonclaves, T., Quaresma, P.: A preliminary approach to the multi-label classification problem of Portuguese juridical documents progress in artificial intelligence, pp. 435–444. Springer, Berlin (2003)

    Google Scholar 

  2. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nedellec, C., Rouveirol, C. (eds.) ECML, LNCS, vol. 1938, pp. 137–142. Springer, Heidelberg (1998)

    Google Scholar 

  3. Luo, X., Zincir Heywood, A.N.: Evaluation of two systems on multi-class multi-label document classification. In: Hacid M.S., Murray N.V., Ras Z.W., Tsumoto S. (eds.) ISMIS 2005, LNCS (LNAI), vol. 3488, pp. 161–169, Springer, Heidelberg (2005)

    Google Scholar 

  4. Tikk, D., Biro, G.: Experiments with multi-label text classifier on the Reuters collection. Proceedings of the International Conference on Computational Cybernetics (ICCC 2003), Hungary, pp. 33–38 (2003)

    Google Scholar 

  5. Yu, K., Yu, S., Tresp, V.: Multi-label informed latent semantic indexing. Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in information retrieval, pp. 258–265 (2005)

    Google Scholar 

  6. Karalic, A., Pirnat, V.: Significance level based multiple tree classification. Informatica 15(5), 12 (1991)

    Google Scholar 

  7. Tsoumakas, G., Katakis, I, Vlahavas, I.: Mining multi-label data, data mining and knowledge discovery handbook. In: Maimon, O., Rokach, L. (ed.) Springer, 2nd edn. (2010)

    Google Scholar 

  8. de Carvalho, A.C., Freitas, A.A.: A tutorial on multi-label classification techniques. Found. Comput. Intell. 5, 177–195 (2009)

    Google Scholar 

  9. Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. Neural Information Processing Systems, NIPS, vol. 14 (2001)

    Google Scholar 

  10. Zhang, M.L., Zhou, Z.H.: A k-nearest neighbour based algorithm for multi-label classification. Proceedings of the 1st IEEE International Conference on Granular Computing, pp. 718–721. Beijing, China (2005)

    Google Scholar 

  11. Boutell, M., Shen, X., Luo, J., Brouwn, C.: Multi-label semantic scene classification, Technical report. Department of Computer Science University of Rochester, USA (2003)

    Google Scholar 

  12. Shen X., Boutell, M., Luo, J., Brown, C.: Multi-label machine learning and its application to semantic scene classification. In: Yeung, M.M., Lienhart, R.W., Li, C.S. (eds.) Storage and retrieval methods and applications for multimedia. Proceedings of the SPIE, vol. 5307, pp. 188–199 (2003)

    Google Scholar 

  13. Zhu, B., Poon, C.K.: Efficient approximation algorithms for multi-label map labelling. In: Algorithms and computation, pp. 143–152. Springer, Heidelberg (1999)

    Google Scholar 

  14. 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 

  15. Sorower, M.S.: A literature survey on algorithms for multi-label learning. Oregon State University, Corvallis (2010)

    Google Scholar 

  16. Elisseeff, A., Weston, J.: Kernel methods for multi-labelled classification and categorical regression problems, Technical report, BIOwulf Technologies (2001)

    Google Scholar 

  17. Tsoumakas, G., Katakis, I.: Multi-label Classification: an overview. Int. J. Data Warehouse. Min. 3(3), 1–13 (2007)

    Article  Google Scholar 

  18. Madjarov, G., Kocev, D., Gjorgjevikj, D., Dzeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45, 3084–3104 (2012)

    Article  Google Scholar 

  19. Wang, N., Sun, J.C., Er, M.J., Liu, Y.C.: A novel extreme learning control framework of unmanned surface vehicles. IEEE Transactions on Cybernetics. Accepted for Publication (2015).

    Google Scholar 

  20. Wang, N., Er, M.J., Han, M.: Generalized single-hidden layer feedforward networks for regression problems. IEEE Transac. Neural Networks Learn. Syst. 26(6), 1161–1176 (2015)

    Article  Google Scholar 

  21. Wang, N., Er, M.J., Han, M.: Parsimonious extreme learning machine using recursive orthogonal least squares. IEEE Transac. Neural Networks Learn. Syst. 25(10), 1828–1841 (2014)

    Article  Google Scholar 

  22. Wang, N., Han, M., Dong, N., Er, M.J.: Constructive multi-output extreme learning machine with application to large tanker motion dynamics identification. Neurocomputing 128, 59–72 (2014)

    Article  Google Scholar 

  23. Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2, 107–122, 06/01 (2011)

    Google Scholar 

  24. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501, 12 (2006)

    Google Scholar 

  25. Ding, S., Zhao, H., Zhang, Y., Xu, X., Nie, R.: Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 1–13 (2013)

    Google Scholar 

  26. Bernardini, F.C., da Silva, R.B., Meza, E.M., das Ostras–RJ–Brazil, R.: Analyzing the influence of cardinality and density characteristics on multi-label learning (2009)

    Google Scholar 

  27. Kongsorot, Y., Horata, P.: Multi-label classification with extreme learning machine. International Conference on Knowledge and Smart Technology, pp. 81–86

    Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of P. R. China (under Grants 51009017 and 51379002), Applied Basic Research Funds from Ministry of Transport of P. R. China (under Grant 2012-329-225-060), China Postdoctoral Science Foundation (under Grant 2012M520629), Program for Liaoning Excellent Talents in University (under Grant LJQ2013055). The second author would like to thank Nanyang Technological University for supporting this work by providing NTU RSS.

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Authors and Affiliations

  1. Nanyang Technological University, Nanyang, Singapore

    Meng Joo Er & Rajasekar Venkatesan

  2. Dalian Maritime University, Dalian, China

    Meng Joo Er & Ning Wang

Authors
  1. Meng Joo Er
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  2. Rajasekar Venkatesan
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  3. Ning Wang
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Corresponding author

Correspondence to Rajasekar Venkatesan .

Editor information

Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

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Er, M.J., Venkatesan, R., Wang, N. (2016). A High Speed Multi-label Classifier Based on Extreme Learning Machines. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_37

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  • DOI: https://doi.org/10.1007/978-3-319-28373-9_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28372-2

  • Online ISBN: 978-3-319-28373-9

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