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.
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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)
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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
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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
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