Dynamic Ensemble Selection and Instantaneous Pruning for Regression Used in Signal Calibration

  • Kaushala Dias
  • Terry Windeatt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

A dynamic method of selecting a pruned ensemble of predictors for regression problems is described. The proposed method enhances the prediction accuracy and generalization ability of pruning methods that change the order in which ensemble members are combined. Ordering heuristics attempt to combine accurate yet complementary regressors. The proposed method enhances the performance by modifying the order of aggregation through distributing the regressor selection over the entire dataset. This paper compares four static ensemble pruning approaches with the proposed dynamic method. The experimental comparison is made using MLP regressors on benchmark datasets and on an industrial application of radio frequency source calibration.

Keywords

Ensemble Pruning Dynamic Ensemble Selection Ensemble Methods Calibration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tsoumakas, G., Partalas, I., Vlahavas, I.: An Ensemble Pruning Primer. In: Okun, O., Valentini, G. (eds.) Applications of Supervised and Unsupervised Ensemble Methods. SCI, vol. 245, pp. 1–13. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Windeatt, T., Zor, C.: Ensemble Pruning Using Spectral Coefficients. IEEE Trans. Neural Network. Learning Syst. 24(4), 673–678 (2013)Google Scholar
  3. 3.
    Martínez-Muñoz, G., Hernández-Lobato, D., Suárez, A.: An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 245–259 (2009)CrossRefGoogle Scholar
  4. 4.
    Hernández-Lobato, D., Martínez-Muñoz, G., Suárez, A.: Empirical Analysis and Evaluation of Approximate Techniques for Pruning Regression Bagging Ensembles. Neurocomputing 74(12-13), 2250–2264 (2011)CrossRefGoogle Scholar
  5. 5.
    Dos Santos, E.M., Sabourin, R., Maupin, P.: A Dynamic Overproduce-and-choose Strategy for the selection of Classifier Ensembles. Pattern Recognition 41, 2993–3009 (2008)CrossRefMATHGoogle Scholar
  6. 6.
    Cavalin, P.R., Sabourin, R., Suen, C.Y.: Dynamic Selection of Ensembles of Classifiers Using Contextual Information. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 145–154. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Dubey, H., Pudi, V.: CLUEKR: Clustering Based Efficient K-NN Regression. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 450–458. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Windeatt, T., Dias, K.: Feature Ranking Ensembles for Facial Action Unit Classification. In: Prevost, L., Marinai, S., Schwenker, F. (eds.) ANNPR 2008. LNCS (LNAI), vol. 5064, pp. 267–279. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Cavalin, P.R., Sabourin, R., Suen, C.Y.: Dynamic Selection Approaches for Multiple Classifier Systems. In: Formal Aspects of Cognitive Processes. LNCS, vol. 22 (3-4), pp. 673–688. Springer (2013)Google Scholar
  10. 10.
    Zhau, Z.-H., Wu, J., Tang, W.: Ensembling Neural Networks: many could be better than all. Artificial Intelligence 137, 239–263 (2002)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Shen, Z.-Q., Kong, F.-S.: Dynamically Weighted Ensemble Neural Networks for Regression Problems. Machine Learning and Cybernetics, 3492–3496 (2004)Google Scholar
  12. 12.
    Mendonca, M., Da Silva, I.N., Castanho, J.E.C.: Camera Calibration Using Neural Networks. Journal of WSCG 10(1-3), POS61–POS68 (2002)Google Scholar
  13. 13.
    Khan, S.A., Shahani, D.T., Agarwala, A.K.: Sensor calibration and compensation using artificial neural network. ISA Transactions 43(3) (2003)Google Scholar
  14. 14.
    Wang, D.-S., Liu, X.-G., Xu, X.-H.: Calibration of Arc-Welding Robot by Neural Network. Fourth International Conference on Machine Learning and Cybernetics, Guangzhou (2005)Google Scholar
  15. 15.
    Liu, E., Cuthbert, L., Schormans, J., Stoneley, G.: Neural Network in Fast Simulation Modelling. IEEE-INNS-ENNS International Joint Conference on Neural Networks 6, 109–113 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kaushala Dias
    • 1
  • Terry Windeatt
    • 1
  1. 1.Centre for Vision, Speech and Signal Processing (CVSSP)University of SurreyGuildfordUK

Personalised recommendations