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Accuracy measures for fuzzy classification in remote sensing Gómez González, Daniel Montero De Juan, Francisco Javier Biging, Greg 004.8 Accuracy assessment Remote sensing Fuzzy classification Inteligencia artificial (Informática) 1203.04 Inteligencia Artificial International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (11ª. 2006. París) Over the last decades, many fuzzy classification algorithms have been proposed for image classification,and in particular to classify those images obtained by remote sensing. But relatively little effort has been done to evaluate goodness or effectiveness of such algorithms. Such a problem is most of the times solved by means of a subjective evaluation, meanwhile in the crisp case quality evaluation can be based upon an error matrix, in which the reference data set (the expert classi-fication) and crisp classifiers data set are been compared using specific accuracy measures. In this paper,some of these measures are translated into the fuzzy case, so that more general accuracy measures between fuzzy classifiers and the reference data set can be considered. Gobierno de España Depto. de Estadística e Investigación Operativa Fac. de Ciencias Matemáticas TRUE pub 2023-06-20T13:41:37Z 2023-06-20T13:41:37Z 2006 book part https://hdl.handle.net/20.500.14352/53408 XXXX-XXXX eng MTM2005-08982 open access application/pdf EDK