Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Fuzzy extreme learning machine for classification

Fuzzy extreme learning machine for classification

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Compared to traditional classifiers, such as SVM, the extreme learning machine (ELM) achieves similar performance for classification and runs at a much faster learning speed. However, in many real applications, the different input points may not be exactly assigned to one of the classes, such as the imbalance problems and the weighted classification problems. The traditional ELM lacks the ability to solve those problems. Proposed is a fuzzy ELM, which introduces a fuzzy membership to the traditional ELM method. Then, the inputs with different fuzzy matrix can make different contributions to the learning of the output weights. For the weighted classification problems, FELM can provide a more logical result than that of ELM.

References

    1. 1)
      • 1. Huang, G.B., Zhu, Q.Y., Siew, C.K.: ‘Extreme learning machine: Theory and applications’. Neurocomputing, 2006, 70, pp. 489501 (doi: 10.1016/j.neucom.2005.12.126).
    2. 2)
      • 6. Lin, C.F., Wang, S.D.: ‘Fuzzy support vector machine’, IEEE Trans. Neural Netw., 2002, 13, (2), pp. 464471 (doi: 10.1109/72.991432).
    3. 3)
      • 3. Huang, G.B., Ding, X.J., Zhou, H.M.: ‘Optimization method based extreme learning machine for classification’, Neurocomputing, 2010, 74, pp. 155163 (doi: 10.1016/j.neucom.2010.02.019).
    4. 4)
      • 4. Huang, G.B., Ding, X.J., Zhou, H.M., Zhang, R.: ‘Extreme learning machine for regression and multiclass classification’. IEEE Trans. Syst. Man Cybern. B, Cybern., 2012, 42, (2), pp. 513529 (doi: 10.1109/TSMCB.2011.2168604).
    5. 5)
      • 2. Liu, Q., He, Q., Shi, Z.: ‘Extreme support vector machine classifier’. Lect. Notes Comput. Sci., 2008, 5012, pp. 222233 (doi: 10.1007/978-3-540-68125-0_21).
    6. 6)
      • 5. Bartlett, P.L.: ‘The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network’, IEEE Trans. Inf. Theory, 1998, 44, (2), pp. 525536 (doi: 10.1109/18.661502).
    7. 7)
      • 7. UCI Repository of Machine Learning Databases, C. Blake, E.Keogh, and C. Merz, 1998, [Online]. Available: http:http://www.ics.uci.edu/mlearn/MLRepository.html.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
      • UCI Repository of Machine Learning Databases, C. Blake, E.Keogh, and C. Merz, 1998, [Online]. Available: http:http://www.ics.uci.edu/mlearn/MLRepository.html.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2012.3642
Loading

Related content

content/journals/10.1049/el.2012.3642
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address