IEEE Intelligent Systems
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Inferring a possibility distribution from empirical data
Fuzzy Sets and Systems
Similarities in fuzzy data mining: from a cognitive view to real-world applications
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Imperfect pattern recognition using the fuzzy measure theory
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Along with possibility theory, fuzzy relation composition rules will be used in our novel approach to deal with the imperfection and the uncertainty that can affect the information elements in any classification system. This takes place at the level of the descriptors of the dataset and the training set objects that can take imprecise, probabilistic, possibilistic, or even missing values, or it happens when assigning classes to the objects associated with different strength degrees. In addition, experts' ambiguous knowledge of the attributes and the objects under consideration must also be pondered in the classification systems. These three types of imperfection will be handled within a simple unified framework, followed by an illustrative detailed example.