Abstract
The choice of a pertinent objective function is one of the most crucial elements in static ensemble selection. In this study, a new approach of calculating the weight of base classifiers is developed. The values of these weights are the basis for the selection process of classifiers from the initial pool. The obtained weights are interpreted in the context of the interval logic. A number of experiments have been carried out on several datasets available in the UCI repository. The performed experiments compare the proposed algorithms with base classifiers, oracle, sum, product, and mean methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Secaucus (2006)
Cavalin, P.R., Sabourin, R., Suen, C.Y.: Dynamic selection approaches for multiple classifier systems. Neural Comput. Appl. 22(3–4), 673–688 (2013)
Cyganek, B.: One-class support vector ensembles for image segmentation and classification. J. Math. Imaging Vis. 42(2–3), 103–117 (2012)
Didaci, L., Giacinto, G., Roli, F., Marciali, G.L.: A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognition, 28, 2188–2191, 11/2005 (2005)
dos Santos, E.M., Sabourin, R.: Classifier ensembles optimization guided by population oracle. In: IEEE Congress on Evolutionary Computation, pp. 693–698 (2011)
Duin, R., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D., Verzakov. S.: PR-Tools4.1, A Matlab Toolbox for Pattern Recognition. Delft University of Technology (2007)
Frank, A., Asuncion, A.: UCI machine learning repository Irvine CA (2010) http://archive.ics.uci.edu/ml
Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recognit. Lett. 22, 25–33 (2001)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Highleyman, W.H.: The design and analysis of pattern recognition experiments. Bell Syst. Tech. J. 41, 723–744 (1962)
Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16(1), 66–75 (1994)
Jackowski, K., Krawczyk, B., Woźniak, M.: Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning. Int. J. Neural Syst. 24(03) (2014)
Jackowski, K., Wozniak, M.: Method of classifier selection using the genetic approach. Expert Syst. 27(2), 114–128 (2010)
Kittler, J., Alkoot, F.M.: Sum versus vote fusion in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 110–115 (2003)
Kuncheva, L.I.: A theoretical study on six classifier fusion strategies. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 281–286 (2002)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley New York (2014)
Lam, L., Suen, C.Y.: Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Trans. Syst. Man, Cybern, Part A 27(5), 553–568 (1997)
Ranawana, R., Palade, V.: Multi-classifier systems: review and a roadmap for developers. Int. J. Hybrid Intell. Syst. 3(1), 35–61 (2006)
Rejer, I.: Genetic algorithms in EEG feature selection for the classification of movements of the left and right hand. In: Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, pp. 579–589. Springer (2013)
Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fusion 6(1), 63–81 (2005)
Smetek, M., Trawinski, B.: Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms. New Gener. Comput. 29(3), 309–327 (2011)
Suen, C.Y., Legault, R., Nadal, C.P., Cheriet, M., Lam, L.: Building a new generation of handwriting recognition systems. Pattern Recognit. Lett. 14(4), 303–315 (1993)
Trawinski, K., Cordon, O., Quirin, A.: A study on the use of multiobjective genetic algorithms for classifier selection in Furia-based fuzzy multiclassifiers. Int. J. Comput. Intell. Syst. 5(2), 231–253 (2012)
Ulas, A., Semerci, M., Yildiz, O.T., Alpaydin, E.: Incremental construction of classifier and discriminant ensembles. Inf. Sci. 179(9), 1298–1318 (2009)
Woloszynski, T., Kurzynski, M.: A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recognit. 44(10–11), 2656–2668 (2011)
Acknowledgments
This work was supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264 and by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Burduk, R. (2015). Method of Static Classifiers Selection Using the Weights of Base Classifiers. In: Wiliński, A., Fray, I., Pejaś, J. (eds) Soft Computing in Computer and Information Science. Advances in Intelligent Systems and Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-15147-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-15147-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15146-5
Online ISBN: 978-3-319-15147-2
eBook Packages: EngineeringEngineering (R0)