Extracting Fuzzy Rules Under Uncertainty and Measuring Definability Using Rough Sets
A. de Korvin, Brian S. Bourgeois, Robert M. Kleyle · Journal of Intelligent & Fuzzy Systems · 1994
In this article we consider attributes to be fuzzy sets. Knowledge acquisition takes place by looking at examples. In each example, attributes as well as a corresponding decision is made available. The decision may be a fuzzy diagnosis. Based on these examples two sets of fuzzy rules are constructed: certain rules and possible rules. Corresponding measures of how much we believe these rules are also constructed. The concept of how much a fuzzy diagnosis is definable in terms of fuzzy attributes is studied. Finally, classifications and some of their properties are analyzed.