Multi-attribute classification using fuzzy integral

Fuzzy set theory can provide a suitable framework for pattern classification, because of the inherent fuzziness involved in the definition of a class or a cluster. Fuzzy set theory is discussed based on a fuzzy pattern matching procedure, where partial matching values with respect to a given attribute are combined. This approach is closely related to a statistical approach to pattern classification. A new method based on a fuzzy integral and possibility theory is presented. A critical examination of the statistical approach and the supervised learning process is outlined. Experimental test results on real data are presented.<>