Abstract
The classic majority voting model can be extended to the spatial domain e.g. to solve object detection problems. However, the detector algorithms cannot be considered as independent classifiers, so a good ensemble cannot be composed by simply selecting the individually most accurate members. In classic theory, diversity measures are recommended that may help to explore the dependencies among the classifiers. In this paper, we generalize the classic diversity measures for the spatial domain within a majority voting framework. We show that these measures fit better to spatial applications with a specific example on object detection on retinal images. Moreover, we show how a more efficient descriptor can be found in terms of a weighted combination of diversity measures which correlates better with the accuracy of the ensemble.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Toman, H., Kovacs, L., Jonas, A., Hajdu, L., Hajdu, A.: A Generalization of Majority Voting Scheme for Medical Image Detectors. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part II. LNCS, vol. 6679, pp. 189–196. Springer, Heidelberg (2011)
Qureshi, R.J., Kovacs, L., Harangi, B., Nagy, B., Peto, T., Hajdu, A.: Combining Algorithms for Automatic Detection of Optic Disc and Macula in Fundus Images. Computer Vision and Image Understanding 116, 138–145 (2012)
Dataset MESSIDOR [Online], http://messidor.crihan.fr
Ruta, D., Gabrys, B.: Classifier Selection for Majority Voting. Information Fusion 6, 63–81 (2005)
Sharkey, A.J.C., Sharkey, N.E.: Combining Diverse Neural Nets. The Knowledge Engineering Review 12, 231–247 (1997)
Ruta, D., Gabrys, B.: Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems. In: Proceedings of the 4th International Symposium on Soft Computing, pp. 50–56 (2001)
Toman, H., Kovacs, L., Jonas, A., Hajdu, L., Hajdu, A.: Generalized Weighted Majority Voting with an Application to Algorithms Having Spatial Output. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part II. LNCS, vol. 7209, pp. 56–67. Springer, Heidelberg (2012)
Kuncheva, L.I.: Combining Pattern Classifiers, Methods and Algorithms. John Wiley & Sons, Inc., New Jersey (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hajdu, A., Hajdu, L., Kovacs, L., Toman, H. (2013). Diversity Measures for Majority Voting in the Spatial Domain. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-40846-5_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40845-8
Online ISBN: 978-3-642-40846-5
eBook Packages: Computer ScienceComputer Science (R0)