Mathematical algorithms for the supervised classification based on fuzzy partial precedence

Some mathematical algorithms for supervised classification problems are presented in this paper. These algorithms are based on fuzzy partial precedents, and they allow us to work with nonclassically described Objects, i.e., mixed data. These types of descriptions frequently arise in soft sciences. As a rule, the most used methods for solving such classification problems are oriented towards one type of feature, most often quantitative. They do not allow the use of different kinds of features, as a consequence, all variables must be quantitative, or exclusively qualitative. In fact, those methods use, in some way, a distance measure between object descriptions, which follows from the hypothesis of compactness of classes. The proposed models allow the handling of quantitative and qualitative features together, and missing values. They are based on partial evaluation of similarity between objects in a fuzzy environment. They do not use a distance.