Improvement of the consistent and strongly selfguessing fuzzy classifiers

Article type: Research Article

Authors: Vatlin, Sergei

Affiliations: Belarussian State University of Informatics and Radioelectronics, P. Brovka 6, 220027 Minsk, Belarus. E-mail: [email protected]

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.

Keywords: machine learning algorithm, fuzzy classifier, probabilistic and monotonic improvement of fuzzy classifiers

DOI: 10.3233/INF-1997-8408

Journal: Informatica, vol. 8, no. 4, pp. 599-605, 1997