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Application of fuzzy logic and genetic algorithms for classification of malignant neoplastic diseases treatments

Abstract:

A fuzzy classifier is a system that assigns a class label to object based on a description of it and makes use of fuzzy sets as part of its operation. Its functioning can be optimized using genetic algorithms, ensuring viable solutions. The paper proposes a fuzzy classifier optimized using a genetic algorithm hybridized with a fuzzy clustering technique. A prototype is implemented and evaluated with synthetic reference data sets as possible classification problems treatments for malignant neoplastic diseases. The results are compared with other classifiers found in the literature on the same test data. The conclusion is that the proposed method obtained similar results with functions easier to interpret.