Self-adaptive Differential Evolutionary Extreme Learning Machine and Its Application in Facial Age Estimation

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International Journal of Advanced Network, Monitoring and Controls

Xi'an Technological University

Subject: Computer Science , Software Engineering

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VOLUME 2 , ISSUE 3 (September 2017) > List of articles

Self-adaptive Differential Evolutionary Extreme Learning Machine and Its Application in Facial Age Estimation

Junhua Ku / Kongduo Xing

Keywords : Extreme learning machines, Differential evolution extreme learning machines, Self-adaptive differential evolution extreme learning machines, Facial age estimation

Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 3, Pages 72-77, DOI: https://doi.org/10.1109/iccnea.2017.31

License : (CC BY-NC-ND 4.0)

Published Online: 11-April-2018

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ABSTRACT

In this paper, Self-adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) was proposed as a new class of learning algorithm for single-hidden layer feed forward neural network (SLFN). In order to achieve good generalization performance, SaDE-ELM calculates the error on a subset of testing data for parameter optimization. Since SaDE-ELM employs extra data for validation to avoid the over fitting 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. Experimental results demonstrate that the proposed algorithms are efficient for Facial Age Estimation.

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REFERENCES

A. Lanitis, C. Draganova, and C. Christodoulou. Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. B, 34(1):621–628, 2004.

 

A. Lanitis, C. J. Taylor, and T. F. Cootes. Toward automatic simulation of aging effects on face images. TPAMI, 24(4):442–455, 2002.

 

Huang GB, Zhu QY, Siew CK. Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks (IJCNN2004), vol 2, no 25–29, pp 985–990.

 

Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501.

 

Espana-Boquera S, Zamora-Martínez F, Castro-Bleda M J, et al. Efficient BP algorithms for general feedforward neural networks[C]//International Work-Conference on the Interplay Between Natural and Artificial Computation. Springer Berlin Heidelberg, 2007: 327-336.

 

G. Thatte, U. Mitra, and J. Heidemann, “Parametric methods for anomaly detection in aggregate traffic,” IEEE/ACM Transactions on Networking, vol. 19, no. 2, pp. 512–525, April 2011.

 

M. Qin and K. Hwang, “Frequent episode rules for internet anomaly detection,” in Proceedings of the Network Computing and Applications, Third IEEE International Symposium. Washington, DC, USA: IEEE Computer Society, 2004, pp. 161–168.

 

X. He, C. Papadopoulos, J. Heidemann, U. Mitra, and U. Riaz, “Remote detection of bottleneck links using spectral and statistical methods,” Computer Networks, vol. 53, pp. 279–298, February 2009.

 

W. W. Streilein, R. K. Cunningham, and S. E. Webster, “Improved detec- tion of low-profile probe and denial-of-service attacks,” in Proceedings of the 2001 Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection, June 2001.

 

C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, pp. 273–297, 1995.

 

G.-B. Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: a survey,” International Journal of Machine Leaning and Cybernetics, vol. 2, no. 2, pp. 107–122, 2011.

 

G. Tandon, “Weighting versus pruning in rule validation for detecting network and host anomalies,” in In Proceedings of the 13th ACM SIGKDD international. ACM Press, 2007.

 

Y. Liao and V. R. Vemuri, “Use of k-nearest neighbor classifier for intrusion detection,” Computers & Security, vol. 25, pp. 439–448, 2002.

 

Storn R, Price K (2004) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

 

Ilonen J, Kamarainen JI, Lampinen J (2003) Differential evolution training algorithm for feedforward neural networks. Neural Process Lett 17:93–105

 

Subudhi B, Jena D (2008) Differential evolution and levenberg marquardt trained neural network scheme for nonlinear system identification. Neural Process Lett 27:285–296.

 

P. Viola and M. J. Jones. Robust real-time face detection. IJCV, 57(2):137–154, 2004.

 

M. Mathias, R. Benenson, M. Pedersoli, and L. Van Gool. Face detection without bells and whistles. In ECCV, 2014, pages 720–735.

 

S. Escalera, M. Torres, B. Martnez, X. Bar, H. J. Escalante, I. Guyon, G. Tzimiropoulos, C. Corneanu, M. Oliu, M. A. Bagheri, and M. Valstar. Chalearn looking at people and faces of the world: Face analysis workshop and challenge 2016. In ChaLearn Looking at People and Faces of the World, CVPRW, 2016

 

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