Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A clustering-based fuzzy classifier
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Fuzzy Classifier Design
Elicitation, assessment, and pooling of expert judgments using possibility theory
IEEE Transactions on Fuzzy Systems
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This work is based on classifiers that can yield possibilistic valuations as output. The valuations may have been obtained from a labeled data set either directly as such, by possibilistic classifiers, by transforming the output of probabilistic classifiers or else by adapting prototype-based classifiers in general. Imprecise classifications are elicited from the possibilistic valuations by varying a parameter that makes the overall classification become more or less precise. We introduce some indices to assess the accuracy of the parameterized imprecise classifications and their reliability, thus allowing the user to choose the most suitable level of imprecision and/or uncertainty for a given application.