Abstract
The overcapacity of the European fishing fleets is one of the recognized factors for the lack of success of the Common Fisheries Policy. Unwanted non-targeted species and other incidental fish likely represent one of the causes for the overexploitation of fish stocks; thus there is a clear connection between this problem and the type of fishing gear used by vessels. This paper performs an environmental impact study of the Spanish Fishing Fleet by means of ordinal classification techniques to emphasize the need to design an effective and differentiated common fish policy for “artisan fleets”, that guarantees the maintenance of environmental stocks and the artesan fishing culture.
This work has been partially subsidized by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain).
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Pérez-Ortiz, M., Colmenarejo, R., Fernández Caballero, J.C., Hervás-Martínez, C. (2013). Can Machine Learning Techniques Help to Improve the Common Fisheries Policy?. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_31
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DOI: https://doi.org/10.1007/978-3-642-38682-4_31
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