Information imperfection processing in supervised classification systems

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.