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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Windeatt, T., Zor, C.: Ensemble Pruning Using Spectral Coefficients. IEEE Trans. Neural Network. Learning Syst. 24(4), 673–678 (2013)
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)
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)
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)
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)
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)
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)
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)
Zhau, Z.-H., Wu, J., Tang, W.: Ensembling Neural Networks: many could be better than all. Artificial Intelligence 137, 239–263 (2002)
Shen, Z.-Q., Kong, F.-S.: Dynamically Weighted Ensemble Neural Networks for Regression Problems. Machine Learning and Cybernetics, 3492–3496 (2004)
Mendonca, M., Da Silva, I.N., Castanho, J.E.C.: Camera Calibration Using Neural Networks. Journal of WSCG 10(1-3), POS61–POS68 (2002)
Khan, S.A., Shahani, D.T., Agarwala, A.K.: Sensor calibration and compensation using artificial neural network. ISA Transactions 43(3) (2003)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Dias, K., Windeatt, T. (2014). Dynamic Ensemble Selection and Instantaneous Pruning for Regression Used in Signal Calibration. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-11179-7_60
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
eBook Packages: Computer ScienceComputer Science (R0)