Inductive Learning From Incomplete and Imprecise Examples

We present an application of fuzzy logic with linguistic quantifiers, mainly of its calculus of linguistically quantified propositions due to Zadeh, in inductive learning under imprecision and errors. The classification into the positive and negative examples is allowed to be to a degrees (of positiveness and negativeness), between 0 and 1. The value of an attribute in an object and in a selector need not be the same allowing for an inexact matching. Errors in the data may exist though their number may be not precisely known. A new inductive learning 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.

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