Abstract
The dynamic selection of classifiers plays an important role in the creation of an ensemble of classifiers. The paper presents the dynamic selection of a posteriori probability function based on the analysis of the decision profiles. The idea of the dynamic selection is exemplified with the binary classification task. In addition, a number of experiments have been carried out on ten benchmark data sets.
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-Verlag New York Inc, Secaucus (2006)
Britto, A.S., Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiers—a comprehensive review. Pattern Recogn. 47(11), 3665–3680 (2014)
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 Vision 42(2–3), 103–117 (2012)
Cyganek, B., Woźniak, M.: Vehicle logo recognition with an ensemble of classifiers. In: Intelligent Information and Database Systems, Lecture Notes in Computer Science, vol. 8398, pp. 117–126. Springer (2014)
Didaci, L., Giacinto, G., Roli, F., Marcialis, G.L.: A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recogn. 38, 2188–2191 (2005)
Forczmański, P., Łabędź, P.: Recognition of occluded faces based on multi-subspace classification. In: Computer Information Systems and Industrial Management, Lecture Notes in Computer Science, vol. 8104, pp. 148–157. Springer (2013)
Frank, A., Asuncion, A.: UCI machine learning repository (2010). http://archive.ics.uci.edu/ml
Frejlichowski, D.: An algorithm for the automatic analysis of characters located on car license plates. In: Image Analysis and Recognition, Lecture Notes in Computer Science, vol. 7950, pp. 774–781. Springer (2013)
Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recogn. Lett. 22, 25–33 (2001)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
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., Woźniak, M.: Method of classifier selection using the genetic approach. Expert Syst. 27(2), 114–128 (2010)
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)
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 (2004)
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)
Przewoźniczek, M., Walkowiak, K., Woźniak, M.: Optimizing distributed computing systems for k-nearest neighbours classifiers-evolutionary approach. Logic J. IGPL 357–372 (2010)
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. Advances in Intelligent Systems and Computing, vol. 226, pp. 579–589. Springer (2013)
Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fusion 6(1), 63–81 (2005)
Smętek, M., Trawiński, 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 Recogn. Lett. 14(4), 303–315 (1993)
Woloszyński, T., Kurzyński, M.: A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recogn. 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
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Baczyńska, P., Burduk, R. (2016). Classifier Selection Uses Decision Profiles in Binary Classification Task. In: Choraś, R. (eds) Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-319-23814-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-23814-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23813-5
Online ISBN: 978-3-319-23814-2
eBook Packages: EngineeringEngineering (R0)