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Moments of Predictive Deviations for Ensemble Diversity Measures to Estimate the Performance of Time Series Prediction

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7667)

Abstract

This paper presents an analysis of moments of predictive deviations as measures of ensemble diversity to estimate the performance of time series prediction. As an extension of the ambiguity decomposition of bagging ensemble, we decompose the fourth power of ensemble prediction error and clarify the effect of the moments of predictive deviations of ensemble members to the ensemble prediction error. We utilize this analysis for estimating the performance of time sires prediction, and show the effectiveness by means of numerical experiments.

Keywords

  • Moments of predictive deviations
  • Ensemble diversity measures
  • Performance estimation
  • Time series prediction

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References

  1. Brown, G., Wyatt, J., Tino, P.: Managing Diversity in Regression Ensembles. J. Mach. Learn. Res. 6, 1621–1650 (2005)

    MathSciNet  MATH  Google Scholar 

  2. Chen, H.: Diversity and Regularization in Neural Network Ensembles. PHD thesis, University of Birmingham (2008)

    Google Scholar 

  3. Kurogi, S.: Improving Generalization Performance via out-of-Bag Estimate Using Variable Size of Bags. J. Japan. Neural Network Society 16, 81–92 (2009)

    CrossRef  Google Scholar 

  4. Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of the Fourteenth International Conference 18 on Artificial Intelligence (IJCAI), pp. 1137–1143 (1995)

    Google Scholar 

  5. Efron, B., Tbshirani, R.: Improvements on Cross-Validation: the.632+ bootstrap method. J. American Stats. Associ. 92, 548–560 (1997)

    MATH  Google Scholar 

  6. Breiman, L.: Bagging Predictors. Mach. Learn. 26, 123–140 (1996)

    Google Scholar 

  7. Aihara, K.: Theories and Applications of Chaotic Time Series Analysis, Sangyo Tosho, Tokyo (2000)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Ono, K., Kurogi, S., Nishida, T. (2012). Moments of Predictive Deviations for Ensemble Diversity Measures to Estimate the Performance of Time Series Prediction. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-34500-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34499-2

  • Online ISBN: 978-3-642-34500-5

  • eBook Packages: Computer ScienceComputer Science (R0)