Abstract
The main focus of this contribution is to present a general methodologyfor the structure optimization of fuzzy classifiers. This approach does not depend on a special type of membership function either it is restricted to small or medium sized input dimension. 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 the diagnosis of malignant or benign type of cancer. Results achieved by using different types of basis functions are presented.
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Grauel, A., Renners, I., Saavedra, E. (2003). Classification Techniques based on Methods of Computational Intelligence. In: Schwaiger, M., Opitz, O. (eds) Exploratory Data Analysis in Empirical Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55721-7_9
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DOI: https://doi.org/10.1007/978-3-642-55721-7_9
Publisher Name: Springer, Berlin, Heidelberg
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