Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The Optimum Class-Selective Rejection Rule
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
A Family of Cluster Validity Indexes Based on a l-Order Fuzzy OR Operator
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A fuzzy modeling approach to cluster validity
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
A family of measures for best top-n class-selective decision rules
Pattern Recognition
Block similarity in fuzzy tuples
Fuzzy Sets and Systems
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In pattern recognition, the membership of an object to classes is often measured by labels. This article mainly deals with the mathematical foundations of labels combination operators, built on t-norms, that extend previous ambiguity measures of objects by dealing not only with two classes ambiguities but also with k classes, k lying between 1 and the number of classes c. Mathematical properties of this family of combination operators are established and a weighted extension is proposed, allowing to give more or less importance to a given class. A classifier with reject options built on the proposed measure is presented and applied on synthetic data. A critical analysis of the results led to derivate some new operators by aggregating previous measures. A modified classifier is proposed and applied to synthetic data as well as to standard real data.