Informatica

Let G0 and G1 be arbitrary fuzzy classifiers (Vatlin, 1993). We say that G1 improves G0 if the performance of G1 is more than G0 one. We also introduced the concepts of consistent and strongly selfguessing fuzzy classifiers. The criterion of strong selfguessing is formulated. The theorems on the conditions of probabilistic improvement of consistent and monotonic improvement of strongly selfguessing fuzzy classifiers are proved.

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.