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Proceedings of ELM-2014 Volume 1 pp 81–90Cite as

Sample-Based Extreme Learning Machine Regression with Absent Data

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Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 3))

Abstract

Regression is of great importance in machine learning community. As one of state-of-the-art algorithms on this regard, extreme learning machine (ELM) has been received intensive attention and successfully applied into regression tasks. However, existing ELM regression algorithms cannot effectively handle the problem of absent data, which is relatively common in practical applications. In this paper, we propose a sample-based ELM regression algorithm to tackle this issue. The corresponding optimization problem is reformulated as a convex one, which can be readily implemented via off-the-shelf optimization packages. We conduct comprehensive experiments on synthetic and UCI benchmark datasets to compare the proposed algorithm with the widely used imputation approaches, including zero-filling and mean-filling. As shown, our algorithm demonstrates superior performance over the compared ones, especially when the absent ratio is relatively intensive.

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References

  1. Huang, G.-B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2(2), 107–122 (2011)

    Article  Google Scholar 

  2. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings. 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990. IEEE (2004)

    Google Scholar 

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

    Article  Google Scholar 

  4. Liu, Q., Yin, J., Leung, V.C., Zhai, J.-H., Cai, Z., Lin, J.: Applying a new localized generalization error model to design neural networks trained with extreme learning machine. Neural Computing and Applications, 1–8 (2014)

    Google Scholar 

  5. Xie, P., Liu, X., Yin, J., Wang, Y.: Absent extreme learning machine algorithm with application to packed executable identification. Neural Computing and Applications, 1–8 (2013)

    Google Scholar 

  6. Huang, G.-B., Zhou, H., Ding, X., Zhang Extreme, R.: learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(2), 513–529 (2012)

    Article  Google Scholar 

  7. Zheng, E., Liu, J., Lu, H., Wang, L., Chen, L.: A new fuzzy extreme learning machine for regression problems with outliers or noises. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013, Part II. LNCS, vol. 8347, pp. 524–534. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. Lan, Y., Soh, Y.C., Huang, G.-B.: Two-stage extreme learning machine for regression. Neurocomputing 73(16), 3028–3038 (2010)

    Article  Google Scholar 

  10. He, Q., Shang, T., Zhuang, F., Shi, Z.: Parallel extreme learning machine for regression based on mapreduce. Neurocomputing 102, 52–58 (2013)

    Article  Google Scholar 

  11. Chang, Y., Wang, S., Tian, H., Zhao, Z.: Multiple regression machine system based on ensemble extreme learning machine for soft sensor. Sensor Letters 11(4), 710–714 (2013)

    Article  Google Scholar 

  12. Royston, P.: Multiple imputation of missing values. Stata Journal 4, 227–241 (2004)

    Google Scholar 

  13. Chechik, G., Heitz, G., Elidan, G., Abbeel, P., Koller, D.: Max-margin classification of data with absent features. The Journal of Machine Learning Research 9, 1–21 (2008)

    MATH  Google Scholar 

  14. Marlin, B.M.: Missing data problems in machine learning. Thesis (2008)

    Google Scholar 

  15. Horton, N.J., Lipsitz, S.R.: Multiple imputation in practice: comparison of software packages for regression models with missing variables. The American Statistician 55(3), 244–254 (2001)

    Article  MathSciNet  Google Scholar 

  16. Yuan, Y.C.: Multiple imputation for missing data: Concepts and new development (version 9.0). SAS Institute Inc., Rockville (2010)

    Google Scholar 

  17. Wang, Y., Cao, F., Yuan, Y.: A study on effectiveness of extreme learning machine. Neurocomputing 74(16), 2483–2490 (2011)

    Article  Google Scholar 

  18. Balasundaram, S.: On extreme learning machine for -insensitiive regression in the primal by newton method. Neural Computing and Applications 22(3-4), 559–567 (2013)

    Article  Google Scholar 

  19. Inc. CVX Research. CVX: Matlab software for disciplined convex programming, version 2.0 (August. 2012), http://cvxr.com/cvx

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Author information

Authors and Affiliations

  1. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China, 410073

    Hang Gao, Xinwang Liu & Yuxing Peng

Authors
  1. Hang Gao
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  2. Xinwang Liu
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  3. Yuxing Peng
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Corresponding author

Correspondence to Hang Gao .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  4. Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia

    Zhihong Man

  5. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of (South Korea)

    Kar-Ann Toh

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Gao, H., Liu, X., Peng, Y. (2015). Sample-Based Extreme Learning Machine Regression with Absent Data. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-14063-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-14063-6_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14062-9

  • Online ISBN: 978-3-319-14063-6

  • eBook Packages: EngineeringEngineering (R0)

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