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Machine Vision and Applications

Erschienen in:

01.03.2017 | Special Issue Paper

Decay-weighted extreme learning machine for balance and optimization learning

verfasst von: Qing Shen, Xiaojuan Ban, Ruoyi Liu, Yu Wang

Erschienen in: Machine Vision and Applications | Ausgabe 7/2017

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Abstract

The original extreme learning machine (ELM) was designed for the balanced data, and it balanced misclassification cost of every sample to get the solution. Weighted extreme learning machine assumed that the balance can be achieved through the equality of misclassification costs. This paper improves previous weighted ELM with decay-weight matrix setting for balance and optimization learning. The decay-weight matrix is based on the sample number of each class, but the weight sum values of each class are not necessarily equal. When the number of samples is reduced, the weight sum is also reduced. By adjusting the decaying velocity, classifier could achieve more appropriate boundary position. From the experimental results, the decay-weighted ELM obtains the better effects in solving the imbalance classification tasks, particularly in multiclass tasks. This method was successfully applied to build the prediction model in the urban traffic congestion prediction system.

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Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)CrossRef

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)CrossRef

Huang, G.B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70(16–18), 3056–3062 (2007)CrossRef

Huang, G.B., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16–18), 3460–3468 (2008)CrossRef

Huang, G.B., Ding, X., Zhou, H.: Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3), 155–163 (2010)CrossRef

Huang, G.B., Zhou, H., Ding, X., et al.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man. Cybern. B Cybern. A Publ. IEEE Syst. Man Cybern. Soc. 42(42), 513–529 (2012)CrossRef

Zhong, H., Miao, C., Shen, Z., et al.: Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings. Neurocomputing 128(5), 285–295 (2014)CrossRef

Huang, G., Song, S., Gupta, J.N., et al.: Semi-supervised and unsupervised extreme learning machines. IEEE Trans. Cybern. 44(12), 2405–2417 (2014)CrossRef

Wang, P., Wang, D., Feng, W.: Online semi-supervised extreme learning machine based on manifold regularization[J]. J. Shanghai Jiaotong Univ. (Sci.) 49(08), 1153–1158 (2015)MathSciNet

10.

Wang, W., Zhang, R.: Improved Convex Incremental Extreme Learning Machine Based on Enhanced Random Search. Unifying Electrical Engineering and Electronics Engineering, pp. 2033–2040. Springer, New York (2014)

11.

Hai-Feng, K.E., Ying, J.: Real-time license character recognition technology based on R-ELM. J. Zhejiang Univ. 48(2), 1209–1216 (2014)

12.

Stolfo, S. J., Fan, W., Lee, W., et al.: Cost-based modeling for fraud and intrusion detection: results from the JAM project. In: DARPA Information Survivability Conference and Exposition, 2000. DISCEX ’00. Proceedings. IEEE Xplore, vol. 2, pp. 130–144 (2000)

13.

Strack, B., Deshazo, J.P., Gennings, C., et al.: Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records.[J]. Biomed. Res. Int. 2014(6), 781670 (2014)

14.

He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)CrossRef

15.

Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. Lecture Notes in Computer Science 3201, 39–50 (2004)

16.

Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Cybern. B Cybern. A Publ. IEEE Syst. Man Cybern. Soc. 39(2), 539–550 (2009)

17.

Chawla, N.V., Lazarevic, A., Hall, L.O., et al.: SMOTEBoost: improving prediction of the minority class in boosting. Lecture Notes in Computer Science 2838, 107–119 (2003)

18.

Deng, W., Zheng, Q., Regularized, Chen L.: Learning, extreme, machine[C], computational intelligence and data mining, : CIDM ’09. IEEE Symposium on. IEEE Xplore 2009, 389–395 (2009)

19.

Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101(3), 229–242 (2013)CrossRef

20.

Fletcher, R.: Practical Methods of Optimization: Constrained Optimization. Practical Methods of Optimization, pp. 71–94. John Wiley, Hoboken (1981)MATH

21.

Lichman, M.: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science (2013)

Titel: Decay-weighted extreme learning machine for balance and optimization learning
verfasst von: Qing Shen
Xiaojuan Ban
Ruoyi Liu
Yu Wang
Publikationsdatum: 01.03.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 7/2017
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-017-0828-4

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