Advertisement

Neural Computing and Applications

, Volume 27, Issue 1, pp 59–66 | Cite as

Applying a new localized generalization error model to design neural networks trained with extreme learning machine

  • Qiang LiuEmail author
  • Jianping Yin
  • Victor C. M. Leung
  • Jun-Hai Zhai
  • Zhiping Cai
  • Jiarun Lin
Extreme Learning Machine and Applications

Abstract

High accuracy and low overhead are two key features of a well-designed classifier for different classification scenarios. In this paper, we propose an improved classifier using a single-hidden layer feedforward neural network (SLFN) trained with extreme learning machine. The novel classifier first utilizes principal component analysis to reduce the feature dimension and then selects the optimal architecture of the SLFN based on a new localized generalization error model in the principal component space. Experimental and statistical results on the NSL-KDD data set demonstrate that the proposed classifier can achieve a significant performance improvement compared with previous classifiers.

Keywords

Extreme learning machine Principal component analysis Architecture selection algorithm Localized generalization error model 

References

  1. 1.
    Abe S (2010) Support vector machines for pattern classification, 2nd edn. Springer, New YorkzbMATHCrossRefGoogle Scholar
  2. 2.
    Chaovalitwongse WA, Jeong YS, Jeong MK, Danish SF, Wong S (2011) Pattern recognition approaches for identifying subcortical targets during deep brain stimulation surgery. IEEE Intell Syst 26(5):54–63CrossRefGoogle Scholar
  3. 3.
    Cheng C, Tay WP, Huang GB (2012) Extreme learning machines for intrusion detection. In: Proceedings of 2012 IJCNN, pp 1–8Google Scholar
  4. 4.
    Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRefGoogle Scholar
  5. 5.
    Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529CrossRefGoogle Scholar
  6. 6.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  7. 7.
    Lan Y, Hu Z, Soh YC, Huang GB (2013) An extreme learning machine approach for speaker recognition. Neural Comput Appl 22(3-4):417–425CrossRefGoogle Scholar
  8. 8.
    Li K, Lu Z, Liu W, Yin J (2012) Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake. Pattern Recognit 45(4):1255–1264CrossRefGoogle Scholar
  9. 9.
    Lin J, Yin J, Cai Z, Liu Q, Li K, Leung VCM (2013) A secure and practical mechanism for outsourcing elms in cloud computing. To be published in IEEE Intell SystGoogle Scholar
  10. 10.
    Liu X, Wang L, Yin J, Zhu E, Zhang J (2013) An efficient approach to integrating radius information into multiple kernel learning. IEEE Trans Cybern 43(2):557–569CrossRefGoogle Scholar
  11. 11.
    Moore DS, McCabe GP, Craig BA (2007) Introduction to the practice of statistics, 6th edn. W. H. Freeman and Company, New YorkGoogle Scholar
  12. 12.
    Sheikhan M, Jadidi Z, Farrokhi A (2012) Intrusion detection using reduced-size rnn based on feature grouping. Neural Comput Appl 21(6):1185–1190CrossRefGoogle Scholar
  13. 13.
    Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the kdd cup 99 data set. In: Proceedings of 2009 IEEE CISDA, pp 1–6Google Scholar
  14. 14.
    Wang XZ, Shao QY, Miao Q, Zhai JH (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102:3–9CrossRefGoogle Scholar
  15. 15.
    Yeung DS, Ng WWY, Wang D, Tsang ECC, Wang XZ (2007) Localized generalization error model and its application to architecture selection for radial basis function neural network. IEEE Trans Neural Netw 18(5):1294–1305CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Qiang Liu
    • 1
    Email author
  • Jianping Yin
    • 2
  • Victor C. M. Leung
    • 3
  • Jun-Hai Zhai
    • 4
  • Zhiping Cai
    • 1
  • Jiarun Lin
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaChina
  3. 3.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverCanada
  4. 4.Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei UniversityBaodingChina

Personalised recommendations