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2018 | OriginalPaper | Chapter

The Use of Geometric Mean in the Process of Integration of Three Base Classifiers

Authors : Robert Burduk, Andrzej Kasprzak

Published in: Computer Information Systems and Industrial Management

Publisher: Springer International Publishing

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Abstract

One of the most important steps in the formation of multiple classifier systems is the integration process also called the base classifiers fusion. The fusion process may be applied either to class labels or confidence levels (discriminant functions). These are the two main methods for combining base classifiers. In this paper, we propose an integration process which takes place in the geometry space. It means that the fusion of base classifiers is done using decision boundaries. In our approach, the final decision boundary is calculated by using the geometric mean. The algorithm presented in the paper concerns the case of 3 basic classifiers and two-dimensional features space. The results of the experiment based on several data sets show that the proposed integration algorithm is a promising method for the development of multiple classifiers systems.

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Literature
1.
go back to reference Britto, A.S., Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiersa comprehensive review. Pattern Recognit. 47(11), 3665–3680 (2014)CrossRef Britto, A.S., Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiersa comprehensive review. Pattern Recognit. 47(11), 3665–3680 (2014)CrossRef
4.
go back to reference Cavalin, P.R., Sabourin, R., Suen, C.Y.: Dynamic selection approaches for multiple classifier systems. Neural Comput. Appl. 22(3–4), 673–688 (2013)CrossRef Cavalin, P.R., Sabourin, R., Suen, C.Y.: Dynamic selection approaches for multiple classifier systems. Neural Comput. Appl. 22(3–4), 673–688 (2013)CrossRef
5.
go back to reference Cyganek, B.: One-class support vector ensembles for image segmentation and classification. J. Math. Imaging Vis. 42(2–3), 103–117 (2012)MathSciNetCrossRef Cyganek, B.: One-class support vector ensembles for image segmentation and classification. J. Math. Imaging Vis. 42(2–3), 103–117 (2012)MathSciNetCrossRef
6.
go back to reference Didaci, L., Giacinto, G., Roli, F., Marcialis, G.L.: A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognit. 38, 2188–2191 (2005)CrossRef Didaci, L., Giacinto, G., Roli, F., Marcialis, G.L.: A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognit. 38, 2188–2191 (2005)CrossRef
7.
go back to reference Drucker, H., Cortes, C., Jackel, L.D., LeCun, Y., Vapnik, V.: Boosting and other ensemble methods. Neural Comput. 6(6), 1289–1301 (1994)CrossRef Drucker, H., Cortes, C., Jackel, L.D., LeCun, Y., Vapnik, V.: Boosting and other ensemble methods. Neural Comput. 6(6), 1289–1301 (1994)CrossRef
8.
go back to reference Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recognit. Lett. 22, 25–33 (2001)CrossRef Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recognit. Lett. 22, 25–33 (2001)CrossRef
9.
go back to reference Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)MATH Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)MATH
10.
go back to reference Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York (2004)CrossRef Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York (2004)CrossRef
11.
go back to reference Li, Y., Meng, D., Gui, Z.: Random optimized geometric ensembles. Neurocomputing 94, 159–163 (2012)CrossRef Li, Y., Meng, D., Gui, Z.: Random optimized geometric ensembles. Neurocomputing 94, 159–163 (2012)CrossRef
12.
go back to reference Ponti, Jr., M.P.: Combining classifiers: from the creation of ensembles to the decision fusion. In: 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pp. 1–10. IEEE (2011) Ponti, Jr., M.P.: Combining classifiers: from the creation of ensembles to the decision fusion. In: 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pp. 1–10. IEEE (2011)
13.
go back to reference Pujol, O., Masip, D.: Geometry-based ensembles: toward a structural characterization of the classification boundary. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1140–1146 (2009)CrossRef Pujol, O., Masip, D.: Geometry-based ensembles: toward a structural characterization of the classification boundary. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1140–1146 (2009)CrossRef
14.
go back to reference Rejer, I.: Genetic algorithms for feature selection for brain computer interface. Int. J. Pattern Recogn. Artif. Intell. 29(5), 1559008 (2015) Rejer, I.: Genetic algorithms for feature selection for brain computer interface. Int. J. Pattern Recogn. Artif. Intell. 29(5), 1559008 (2015)
15.
go back to reference Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fusion 6(1), 63–81 (2005)CrossRef Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fusion 6(1), 63–81 (2005)CrossRef
17.
go back to reference Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)CrossRef Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)CrossRef
18.
go back to reference Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)CrossRef Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)CrossRef
Metadata
Title
The Use of Geometric Mean in the Process of Integration of Three Base Classifiers
Authors
Robert Burduk
Andrzej Kasprzak
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-99954-8_21

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