Abstract
Due to its much faster speed and better generalization performance, extreme learning machine (ELM) has attracted many attentions as an effective learning approach. However, ELM rarely involves strategies for imbalanced data distributions which may exist in many fields. In this paper, we will propose a novel imbalanced extreme learning machine (Im-ELM) algorithm for binary classification problems, which is applicable to the cases with both balanced and imbalanced data distributions, by addressing the classification errors for each class in the performance index, and determining the design parameters through a two-stage heuristic search method. Detailed performance comparison for Im-ELM is done based on a number of benchmark datasets for binary classification. The results show that Im-ELM can achieve better performance for classification problems with imbalanced data distributions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hornik, K.: Approxination capabilities of multilayer feedforword networks. Neural Netw. 4(2), 251–257 (1991)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomptuing 70(1), 489–501 (2006)
Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879–892 (2006)
Huang, G.B., Chen, L.: Convex incremental extreme learning machine. Neurocomptuing 70(10), 3056–3062 (2007)
Huang, G.B., Li, M.B., Chen, L., et al.: Incremental extreme learning machine with fully complex hidden nodes. Neurocomptuing 71(4), 576–583 (2008)
Huang, G.B., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomptuing 71(16), 3056–3062 (2008)
Liang, N.Y., Huang, G.B., Saratchandran, P., et al.: A fast and accurate online squential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17(6), 1411–1423 (2006)
Huang, G., Song, S., Gupta, J.N., et al.: Semi-supervised and unsupervised extreme learning machines. IEEE Trans. Cybern. 78(3), 2405–2417 (2014)
He, Y.L., Geng, Z.Q., Xu, Y., et al.: A hierarchical structure of extreme learning machine (HELM) forhigh-dimensional datasets with noise. Neurocomputing 76(3), 407–414 (2014)
Zhang, S., Chen, X., Yin, Y.X.: An ELM based online soft sensing approach for alumina concentration detection. Math. Probl. Eng. 2015, Article ID 268132, 8 pp (2015)
Xiao, W.D., Lu, Y.J.: Daily human physical activity recognition based on kernel discriminant analysis and extreme learning machine. Math. Probl. Eng. 2015, Article ID 790412, 8 pp (2015)
Soria-Olivas, E., Gomez-Sanchis, J., Martin, J.D., et al.: BELM: Bayesian extreme learing machine. IEEE Trans. Neural Netw. 22(3), 505–509 (2011)
Rong, H.J., Ong, Y.S., Tan, A.H., et al.: A fast pruned-extreme learning machine for classification problem. Neurocomputing 72(3), 359–366 (2008)
Huang, G.B., Zhou, H., Ding, X., et al.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B Cybern. 42(2), 513–529 (2012)
Zong, W.W., Huang, G.B., Chen, Y.Q.: Weighted extreme learning machine for imbalance learing. Neurocomputing 101(1), 229–242 (2013)
Rao, C.R., Mitra, S.K.: Generalized Inverse of Matrices and its Applications. Wiley, New York (1971)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Deng, W.Y., Chen, L.: Regularized extreme learning machine. In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 389–395. IEEE Press, USA (2009)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Fung, G., Mangasarian, O.L.:Proximal Support Vector Machine Classifiers. In: Proceedings KDD-2001: Knowledge Discovery and Data Mining, pp. 77–86 (2001)
He, H.B., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Thai-Nghe, N., Gantner, Z., Schmidt-Thieme, L.: A new evaluation measure for learning from imbalanced data. In: Proceedings of International Joint Conference on Neural Networks, pp. 537–542 (2011)
Fawcett, T.: An Introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Tang, Y.C., Zhang, Y.Q., Chawla, N.C., et al.: SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 39(1), 281–288 (2009)
Huang, G.B.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 6, 376–390 (2014)
Fernandez, A., Jesus, M.J., Herrera, F.: Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets. Int. J. Approx. Reason. 3(50), 561–577 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Xiao, W., Zhang, J., Li, Y., Yang, W. (2016). Imbalanced Extreme Learning Machine for Classification with Imbalanced Data Distributions. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-28373-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28372-2
Online ISBN: 978-3-319-28373-9
eBook Packages: EngineeringEngineering (R0)