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)
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, that may have been obtained from a labeled data set either directly as such, by possibilistic classifiers, or 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 either more or less precise. We discuss some accu-racy measures to assess the quality of the parameterized imprecise classifications, thus allowing the user to choose the most suitable level of imprecision for a given application. Here we particularly address the issue of aggregating parameterized aggregation classifiers, and assessing their performance.