Abstract
Extreme learning machine (ELM) is a fast algorithm to train single-hidden layer feedforward neural networks (SLFNs). Like the traditional classification algorithms, such as decision tree, Naïve Bayes classifier and support vector machine, ELM also tends to provide biased classification results when the classification tasks are imbalanced. In this article, we first analyze the relationship between ELM and Naïve Bayes classifier, and then take the decision outputs of all training instances in ELM as probability density representation by kernel probability density estimation method. Finally, the optimal classification hyperplane can be determined by finding the intersection point of two probability density distribution curves. Experimental results on thirty-two imbalanced data sets indicate that the proposed algorithm can address class imbalance problem effectively, as well outperform some existing class imbalance learning algorithms in the context of ELM.
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References
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42, 513–529 (2012)
Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machine: a review. Neural Netw. 61, 32–48 (2015)
Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101, 229–242 (2013)
Zhang, W.B., Ji, H.B.: Fuzzy extreme learning machine for classification. IET Electron. Lett. 49, 448–450 (2013)
Vong, C.M., Ip, W.F., Wong, P.K., Chiu, C.C.: Predicting minority class for suspended particulate matters level by extreme learning machine. Neurocomputing 128, 136–144 (2014)
Sun, S.J., Chang, C., Hsu, M.F.: Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction. Knowl. Based Syst. 39, 214–223 (2013)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21, 1263–1284 (2009)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagation errors. Nature 323, 533–536 (1986)
Ruck, D.W., Rogers, S.K., Kabrisky, M., Oxley, M.E., Suter, B.W.: The multilayer perceptron as an approximation to a Bayes optimal discriminant function. IEEE Trans. Neural Netw. 1, 296–298 (1990)
Wan, E.A.: Neural network classification: a Bayesian interpretation. IEEE Trans. Neural Netw. 1, 303–305 (1990)
Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065–1076 (1962)
Alcalá-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17, 255–287 (2011)
Acknowledgements
This work was supported in part by National Natural Science Foundation of China under grant No. 61305058, Natural Science Foundation of Jiangsu Province of China under grant No. BK20130471, and China Postdoctoral Science Foundation under grant No. 2013M540404 and No. 2015T80481.
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Yang, J., Yu, H., Yang, X., Zuo, X. (2015). Imbalanced Extreme Learning Machine Based on Probability Density Estimation. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_15
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DOI: https://doi.org/10.1007/978-3-319-26181-2_15
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