Skip to main content

2016 | OriginalPaper | Buchkapitel

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

verfasst von : Meng Joo Er, Rajasekar Venkatesan, Ning Wang

Erschienen in: Proceedings of ELM-2015 Volume 2

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat 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) 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)
2.
Zurück zum Zitat 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) 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)
3.
Zurück zum Zitat 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) 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)
4.
Zurück zum Zitat 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) 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)
5.
Zurück zum Zitat 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) 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)
6.
Zurück zum Zitat Karalic, A., Pirnat, V.: Significance level based multiple tree classification. Informatica 15(5), 12 (1991) Karalic, A., Pirnat, V.: Significance level based multiple tree classification. Informatica 15(5), 12 (1991)
7.
Zurück zum Zitat 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) 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)
8.
Zurück zum Zitat de Carvalho, A.C., Freitas, A.A.: A tutorial on multi-label classification techniques. Found. Comput. Intell. 5, 177–195 (2009) de Carvalho, A.C., Freitas, A.A.: A tutorial on multi-label classification techniques. Found. Comput. Intell. 5, 177–195 (2009)
9.
Zurück zum Zitat Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. Neural Information Processing Systems, NIPS, vol. 14 (2001) Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. Neural Information Processing Systems, NIPS, vol. 14 (2001)
10.
Zurück zum Zitat 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) 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)
11.
Zurück zum Zitat Boutell, M., Shen, X., Luo, J., Brouwn, C.: Multi-label semantic scene classification, Technical report. Department of Computer Science University of Rochester, USA (2003) Boutell, M., Shen, X., Luo, J., Brouwn, C.: Multi-label semantic scene classification, Technical report. Department of Computer Science University of Rochester, USA (2003)
12.
Zurück zum Zitat 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) 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)
13.
Zurück zum Zitat Zhu, B., Poon, C.K.: Efficient approximation algorithms for multi-label map labelling. In: Algorithms and computation, pp. 143–152. Springer, Heidelberg (1999) Zhu, B., Poon, C.K.: Efficient approximation algorithms for multi-label map labelling. In: Algorithms and computation, pp. 143–152. Springer, Heidelberg (1999)
14.
Zurück zum Zitat Zhang, M.L., Zhou, Z.H.: ML-kNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)CrossRefMATH Zhang, M.L., Zhou, Z.H.: ML-kNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)CrossRefMATH
15.
Zurück zum Zitat Sorower, M.S.: A literature survey on algorithms for multi-label learning. Oregon State University, Corvallis (2010) Sorower, M.S.: A literature survey on algorithms for multi-label learning. Oregon State University, Corvallis (2010)
16.
Zurück zum Zitat Elisseeff, A., Weston, J.: Kernel methods for multi-labelled classification and categorical regression problems, Technical report, BIOwulf Technologies (2001) Elisseeff, A., Weston, J.: Kernel methods for multi-labelled classification and categorical regression problems, Technical report, BIOwulf Technologies (2001)
17.
Zurück zum Zitat Tsoumakas, G., Katakis, I.: Multi-label Classification: an overview. Int. J. Data Warehouse. Min. 3(3), 1–13 (2007)CrossRef Tsoumakas, G., Katakis, I.: Multi-label Classification: an overview. Int. J. Data Warehouse. Min. 3(3), 1–13 (2007)CrossRef
18.
Zurück zum Zitat Madjarov, G., Kocev, D., Gjorgjevikj, D., Dzeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45, 3084–3104 (2012)CrossRef Madjarov, G., Kocev, D., Gjorgjevikj, D., Dzeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45, 3084–3104 (2012)CrossRef
19.
Zurück zum Zitat 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). 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).
20.
Zurück zum Zitat 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)CrossRef 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)CrossRef
21.
Zurück zum Zitat 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)CrossRef 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)CrossRef
22.
Zurück zum Zitat 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)CrossRef 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)CrossRef
23.
Zurück zum Zitat Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2, 107–122, 06/01 (2011) Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2, 107–122, 06/01 (2011)
24.
Zurück zum Zitat Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501, 12 (2006) Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501, 12 (2006)
25.
Zurück zum Zitat Ding, S., Zhao, H., Zhang, Y., Xu, X., Nie, R.: Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 1–13 (2013) Ding, S., Zhao, H., Zhang, Y., Xu, X., Nie, R.: Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 1–13 (2013)
26.
Zurück zum Zitat 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) 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)
27.
Zurück zum Zitat Kongsorot, Y., Horata, P.: Multi-label classification with extreme learning machine. International Conference on Knowledge and Smart Technology, pp. 81–86 Kongsorot, Y., Horata, P.: Multi-label classification with extreme learning machine. International Conference on Knowledge and Smart Technology, pp. 81–86
Metadaten
Titel
A High Speed Multi-label Classifier Based on Extreme Learning Machines
verfasst von
Meng Joo Er
Rajasekar Venkatesan
Ning Wang
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-28373-9_37