Improvement of the consistent and strongly selfguessing fuzzy classifiers

Abstract

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.