Informatica

The problem of construction of the fuzzy classification models (fuzzy classifiers) with high generalization ability is discussed. The strong self guessing property of fuzzy classificational models is introduced and examined. It is proved that this characteristic doesn't form a full system of restrictions, i.e., for the unambiguous detection of the most valid fuzzy classifier (among the set of fuzzy classifiers agreed with arbitrary learning set) it is necessary to use additional “regularizing” restrictions.

A possible interpretation, in terms of fuzzy classification models (fuzzy classifiers), of one of the general principles of choosing a scientific theory – a consistency principle – is considered. A concept of a stability measure of unsupervised fuzzy classifiers is introduced. A general scheme of computing the above measure is proposed. A concrete algorithm for implementing the general scheme and examples of its application are given.

A possible interpretation, in terms of fuzzy classification models (fuzzy classifiers), of one of the general principles of choosing a scientific theory – a consistency principle – is considered. Supervised self-guessing fuzzy classifiers are determined. A theorem on character of restrictions induced on a set of supervised fuzzy classifiers by a self-guessing requirement is proved. Feasible alternatives of using the self-guessing property while constructing supervised fuzzy classifiers are analyzed.

The basic properties and methods of developing max-min and max-Δ transitive approximations of resemblance matrices of observed objects are reviewed. A new algorithm of constructing max-Δ transitive closure of such matrices is presented. The conditions of applications of the max-min and max-Δ transitive measures of similarity are considered.