Application of modified fuzzy clustering to medical data classification

• Autorzy: Jeżewski M.
• Języki publikacji: EN

Abstrakty

EN

Classification plays very important role in medical diagnosis. This paper presents fuzzy clustering method dedicated to classification algorithms. It focuses on two additional sub-methods modifying obtained clustering prototypes and leading to final prototypes, which are used for creating the classifier fuzzy if-then rules. The main goal of that work was to examine a performance of the classifier which uses such rules. Commonly used including medical benchmark databases were applied. In order to validate the results, each database was represented by 100 pairs of learning and testing subsets. The obtained classification quality was better in relation to the one of the best classifiers - Lagrangian SVM and suggests that presented clustering with additional sub-methods are appropriate to application to classification algorithms.

Słowa kluczowe

EN

fuzzy clustering; classification; medical data

PL

grupowanie rozmyte; klasyfikacja; dane medyczne

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2011
• Tom: Vol. 17
• Strony: 51--57
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Jeżewski M.

• Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

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