Abstract
The Extreme Learning Machine (ELM), recently proposed by Huang et al. [6], is a single-hidden-layered neural network architecture which has been successfully applied to nonlinear regression and classification tasks [5]. A crucial step in the design of the ELM is the computation of the output weight matrix, a step usually performed by means of the ordinary least-squares (OLS) method - a.k.a. Moore-Penrose generalized inverse technique. However, it is well-known that the OLS method produces predictive models which are highly sensitive to outliers in the data. In this paper, we develop an extension of ELM which is robust to outliers caused by labelling errors. To deal with this problem, we suggest the use of M-estimators, a parameter estimation framework widely used in robust regression, to compute the output weight matrix, instead of using the standard OLS solution. The proposed model is robust to label noise not only near the class boundaries, but also far from the class boundaries which can result from mistakes in labelling or gross errors in measuring the input features. We show the usefulness of the proposed classification approach through simulation results using synthetic and real-world data.
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References
Deng, W., Zheng, Q., Chen, L.: Regularized extreme learning machine. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, pp. 389–395 (2009)
Fox, J.: Applied Regression Analysis, Linear Models, and Related Methods. Sage Publications (1997)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Horata, P., Chiewchanwattana, S., Sunat, K.: Robust extreme learning machine. Neurocomputing 102, 31–44 (2012)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2, 107–122 (2011)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006)
Huber, P.J.: Robust estimation of a location parameter. Annals of Mathematical Statistics 35(1), 73–101 (1964)
Huber, P.J., Ronchetti, E.M.: Robust Statistics. John Wiley & Sons, LTD. (2009)
Kim, H.-C., Ghahramani, Z.: Outlier robust gaussian process classification. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) SSPR&SPR 2008. LNCS, vol. 5342, pp. 896–905. Springer, Heidelberg (2008)
Lee, C.C., Chiang, Y.C., Shih, C.Y., Tsai, C.L.: Noisy time series prediction using m-estimator based robust radial basis function neural networks with growing and pruning techniques. Expert Systems and Applications 36(3), 4717–4724 (2009)
Lee, C.C., Chung, P.C., Tsai, J.R., Chang, C.I.: Robust radial basis function neural networks. IEEE Transactions on Systems, Man, and Cybernetics - Part B 29(6), 674–685 (1999)
Li, D., Han, M., Wang, J.: Chaotic time series prediction based on a novel robust echo state network. IEEE Transactions on Neural Networks and Learning Systems 23(5), 787–799 (2012)
Liu, N., Wang, H.: Ensemble based extreme learning machine. IEEE Signal Processing Letters 17(8), 754–757 (2010)
Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., Lendasse, A.: OP-ELM: Optimally pruned extreme learning machine. IEEE Transactions on Neural Networks 21(1), 158–162 (2010)
Miche, Y., van Heeswijk, M., Bas, P., Simula, O., Lendasse, A.: TROP-ELM: a double-regularized ELM using LARS and Tikhonov regularization. Neurocomputing 74(16), 2413–2421 (2011)
Mohammed, A., Minhas, R., Jonathan Wu, Q.M., Sid-Ahmed, M.A.: Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recognition 44(10-11), 2588–2597 (2011)
Neumann, K., Steil, J.: Optimizing extreme learning machines via ridge regression and batch intrinsic plasticity. Neurocomputing 102, 23–30 (2013)
Zong, W., Huang, G.B.: Face recognition based on extreme learning machine. Neurocomputing 74(16), 2541–2551 (2011)
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Barros, A.L.B.P., Barreto, G.A. (2013). Building a Robust Extreme Learning Machine for Classification in the Presence of Outliers. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_59
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DOI: https://doi.org/10.1007/978-3-642-40846-5_59
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