Abstract
Oceanographers from the IFREMER institute have an hypothesis that the presence of so-called “retentive” meso-scale vortices in ocean and coastal waters could have an influence on watery fauna’s demography. Up to now, identification of retentive hydro-dynamical structures on stream maps has been performed by experts using background knowledge about the area. We tackle this task with filters induced by Genetic Programming, a technique that has already been successfully used in pattern matching problems. To overcome specific difficulties associated with this problem, we introduce a refined scheme that iterates the filters classification phase while giving them access to a memory of their previous decisions. These iterative filters achieve superior results and are compared to a set of other methods.
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Segond, M., Fonlupt, C., Robilliard, D.: Ant Algorithms for Detection in Coastal Waters. In: EA 2003, vol. 1, pp. 1–100 (2003)
Daïda, J.M., Bersano-Begey, C.T.F., Ross, S.J., Vesecky, J.F.: Computer Assisted Design of Image Classification Algorithms: Dynamic and Static Fitness Evaluations in a Scaffolded Genetic Programming Environment. In: GP (1996)
Corpetti, T., Mémin, E., Pérez, P.: Dense estimation of fluid flows. IEEE transaction on pattern analysis and machine intelligence 24(3), 365–380 (2002)
Corpetti, T., Mémin, E., Pérez, P.: Extraction of singular points from dense motion fields: an analytic approach. Journal of mathematical imaging and vision (2003)
Sebag, M., Azé, J., Lucas, N.: ROC-Based Evolutionary Learning: Application to Medical Data Mining. Artificial Evolution 2003, 384–396 (2003)
Langdon, W.B., Buxton, B.F.: Evolving receiver operating characteristics for data fusion. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 87–96. Springer, Heidelberg (2001)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
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Segond, M., Robilliard, D., Fonlupt, C. (2006). Iterative Filter Generation Using Genetic Programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds) Genetic Programming. EuroGP 2006. Lecture Notes in Computer Science, vol 3905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11729976_13
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DOI: https://doi.org/10.1007/11729976_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33143-8
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