This paper proposes a new accuracy evaluation method within a behavioral comparison strategy which uses interval type-2 fuzzy sets and derived operations to model reference data and define soft accuracy indexes. The method addresses the case in which grades of membership, collected by surveying experts, will often be different for the same reference pattern, because the experts will not necessarily be in agreement. The approach is illustrated using simple examples and an application in the domain of biomedical image segmentation.

Accuracy Evaluation of Soft Classifiers using Interval Type-2 Fuzzy Sets Framework

BINAGHI, ELISABETTA;VERGANI, ALBERTO ARTURO;
2017-01-01

Abstract

This paper proposes a new accuracy evaluation method within a behavioral comparison strategy which uses interval type-2 fuzzy sets and derived operations to model reference data and define soft accuracy indexes. The method addresses the case in which grades of membership, collected by surveying experts, will often be different for the same reference pattern, because the experts will not necessarily be in agreement. The approach is illustrated using simple examples and an application in the domain of biomedical image segmentation.
2017
Proceedings of IEEE International Conference on Fuzzy Systems
9781509060344
IEEE International Conference on Fuzzy Systems
Napoli
July 9-12, 2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2060284
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