Abstract
Extreme learning machine (ELM) as one new learning algorithm has been proposed for single hidden-layer feed-forward neural network (SLFN). In contrast with the popular back-propagation (BP) algorithm, ELM often has obviously faster learning speed and stronger generalization performance. However, ELM lacks stability as the weights and biases between the input layer and the hidden layer are randomly assigned, and meanwhile, it often suffers from overfitting as the learning model will approximate all training instances well. In this article, a dynamic generation approach for ensemble of extreme learning machine (DELM) is proposed to overcome the problems above. Specifically, cross-validation and one target function are embedded into the learning phase. Experimental results on several benchmark datasets indicate that DELM is robust and accurate.
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References
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagation errors. Nature 323, 533–536 (1986)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machine: a survey. International Journal of Machine Learning and Cybernetics 2, 107–122 (2011)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42, 513–529 (2012)
Choi, K., Toh, K.A., Byun, H.: Realtime training on mobile devices for face recognition applications. Pattern Recognition 44, 386–400 (2011)
Minhas, R., Baradarani, A., Seifzadeh, S., Wu, Q.M.J.: Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing 73, 1906–1917 (2010)
Sun, Z.L., Choi, T.M., Au, K.F., Yu, Y.: Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems 46, 411–419 (2008)
Chen, F.L., Ou, T.Y.: Sales forecasting system based on gray extreme learning machine with Taguchi method in retail industry. Expert Systems with Applications 38, 1336–1345 (2011)
Li, F.C., Wang, P.K., Wang, G.E.: Comparison of the primitive classifiers with extreme learning machine in credit scoring. In: 16th International Conference on Industrial Engineering and Engineering Management, pp. 685–688. IEEE Press, New York (2009)
Li, L.N., Ouyang, J.H., Chen, H.L., Liu, D.Y.: A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine. Journal of Medical Systems 36, 3327–3337 (2012)
Lan, Y., Soh, Y.C., Huang, G.B.: Ensemble of online sequential extreme learning machine. Neurocomputing 72, 3391–3395 (2009)
Liu, N., Wang, H.: Ensemble based extreme learning machine. IEEE Signal Processing Letters 17, 754–757 (2010)
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems 7, 231–238 (1995)
Zhou, Z.H., Wu, J., Tang, W.: Ensembling neural networks: Many could be better than all. Artificial Intelligence 137, 239–263 (2002)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Tang, E.K., Suganthan, P.N., Yao, X.: An analysis of diversity measures. Machine Learning 65, 247–271 (2006)
Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science, University of California. http://archive.ics.uci.edu/ml/
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Yu, H., Yuan, Y., Yang, X., Dan, Y. (2014). A Dynamic Generation Approach for Ensemble of Extreme Learning Machines. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_33
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DOI: https://doi.org/10.1007/978-3-319-12436-0_33
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