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Evolutionary Extreme Learning Machine and Its Application to Image Analysis

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Abstract

Extreme learning machine (ELM) and evolutionary ELM (E-ELM) were proposed as a new class of learning algorithm for single-hidden layer feedforward neural network (SLFN). In order to achieve good generalization performance, E-ELM calculates the error on a subset of testing data for parameter optimization. Since E-ELMemploys extra data for validation to avoid the overfitting problem, more samples are needed for model training. In this paper, the cross-validation strategy is proposed to be embedded into the training phase so as to solve the overtraining problem. Based on this new learning structure, two extensions of E-ELM are introduced. Experimental results demonstrate that the proposed algorithms are efficient for image analysis.

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References

  1. Belhumeur, P.N., Hespanha, J.P., Kriegman, J.D. (1997). Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 711–720.

    Article  Google Scholar 

  2. Chen, L., Man, H., Nefian, V.A. (2005). Face recognition based on multi-class mapping of fisher scores. Pattern Recognition, 38, 799–811.

    Article  Google Scholar 

  3. Chen, W.L., Er, M.J., Wu, S.Q. (2006). Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36, 458–466.

    Article  Google Scholar 

  4. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.

    MATH  Google Scholar 

  5. Duda, R.O., Hart, P.E., Stork, D.G. (2001). Pattern classification. New York: Wiley.

    MATH  Google Scholar 

  6. Graham, D.B., & Allinson, N.M. (1998). Characterizing virtual eigensignatures for general purpose face recognition In H. Wechsler, P.J. Phillips, V. Bruce, F. Fogelman-Soulie, T.S. Huang (Eds.), Face recognition: From theory to applications, NATO ASI series F, computer and systems sciences (Vol. 163, pp. 446–456).

  7. Hafed, Z.M., & Levine, M.D. (2001). Face recognition using the discrete cosine transform. International Journal of Computer Vision, 43, 167–188.

    Article  MATH  Google Scholar 

  8. Huang, G.B., Chen, L., Siew, C.K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17, 879–892.

    Article  Google Scholar 

  9. Huang, G.B., Li, M.B., Chen, L., Siew, C.K. (2008). Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing, 71, 576–583.

    Article  Google Scholar 

  10. Huang, G.B., Wang, D.H., Lan, Y. (2011). Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics, 2, 107–122.

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17, 491–502.

    Article  Google Scholar 

  13. Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N. (2009). Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition. IEEE Transactions on Neural Networks, 20, 103–123.

    Article  Google Scholar 

  14. Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N. (2005). Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recognition Letters, 26, 181–191.

    Article  Google Scholar 

  15. Moghaddam, B., Jebara, T., Pentland, A. (2000). Bayesian face recognition. Pattern Recognition, 33, 1771–1782.

    Article  Google Scholar 

  16. Nixon, M., & Aguado, A.S. (2008). Feature extraction and image processing. Oxford: Academic Press.

    Google Scholar 

  17. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J. (2000). The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1090–1104.

    Article  Google Scholar 

  18. Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6, 21–45.

    Article  Google Scholar 

  19. Rong, H.J., Ong, Y.S., Tan, A.H., Zhu, Z.X. (2008). A fast pruned-extreme learning machine for classification problem. Neurocomputing, 72, 359–366.

    Article  Google Scholar 

  20. Samaria, F.S. (1994). Face recognition using hidden markov models. PhD thesis, University of Cambridge, Cambridge.

  21. Serre, D. (2002). Matrices: Theory and applications. New York: Springer.

    Google Scholar 

  22. Storn, R., & Price, K. (1997). Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.

    Article  MathSciNet  MATH  Google Scholar 

  23. Wang, X.Z., & Dong, C.R. (2009). Improving generalization of fuzzy if–then rules by maximizing fuzzy entropy. IEEE Transactions on Fuzzy Systems, 17, 556–567.

    Article  Google Scholar 

  24. Wang, X.Z., Zhai, J.H., Lu, S.X. (2008). Induction of multiple fuzzy decision trees based on rough set technique. Information Sciences, 178, 3188–3202.

    Article  MathSciNet  MATH  Google Scholar 

  25. Ye, J. (2005). Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. Journal of Machine Learning Research, 6, 483–502.

    MATH  Google Scholar 

  26. Zhai, J.H., Xu, H.Y., Wang, X.Z. (2012). Dynamic ensemble extreme learning machine based on sample entropy. Soft Computing, 16, 1493–1502.

    Article  Google Scholar 

  27. Zhang, R.X., Huang, G.B., Sundararajan, N., Saratchandran, P. (2007). Multicategory classification using extreme learning machine for microarray gene expression cancer diagnosis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4, 485–495.

    Article  Google Scholar 

  28. Zhu, Q.Y., Qin, A.K., Suganthan, P.N., Huang, G.B. (2005). Evolutionary extreme learning machine. Pattern Recognition, 38, 1759–763.

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore

    Nan Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Han Wang

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  1. Nan Liu
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  2. Han Wang
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Correspondence to Nan Liu.

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Liu, N., Wang, H. Evolutionary Extreme Learning Machine and Its Application to Image Analysis. J Sign Process Syst 73, 73–81 (2013). https://doi.org/10.1007/s11265-013-0730-x

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  • DOI: https://doi.org/10.1007/s11265-013-0730-x

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