Abstract
In this paper a methodology using evolutionary algorithms is introduced for the optimization of fuzzy classifiers based on B-splines. The proposed algorithm maximizes the performance and minimizes the size of the classifier. On a well-known classification problem the algorithm performs an input selection over 9 observed characteristics yielding in a statement which attributes are important with respect to diagnose malignant or benign type of cancer. © Springer-Verlag 2001.
Cite
CITATION STYLE
Renners, I., Grauel, A., & Saavedra, E. (2001). Methodology for optimizing fuzzy classifiers based on computational intelligence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2206 LNCS, pp. 412–419). Springer Verlag. https://doi.org/10.1007/3-540-45493-4_42
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