Fuzzy logic with linguistic quantifiers in inductive learning

A new, fuzzy-logic-based approach to inductive learning under imprecision and errors is proposed. We assume, first, that the classification into the positive and negative examples is to a degrees (of positiveness and negativeness), between 0 and 1, second, that the value of an attribute in an object and in a selector need not be the same allowing for an inexact matching between a concept description and an object, and third, that errors in the data may exist though their number is not precisely known. The problem is formulated as to find a concept description which best satisfies, say, almost all of the positive examples and almost none of the negative ones. A fuzzy-logic-based calculus of linguistically quantified propositions is employed.

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