Approximate Reasoning based on Linguistic Modifiers in a Learning System

Approximate reasoning, initially introduced in fuzzy logic context, allows reasoning with imperfect knowledge. We have proposed in a previous work an approximate reasoning based on linguistic modifiers in a symbolic context. To apply such reasoning, a base of rules is needed. We propose in this paper to use a supervised learning system named SUCRAGE, that automatically generates multi-valued classification rules. Our reasoning is used with this rule base to classify new objects. Experimental tests and comparative study with two initial reasoning modes of SUCRAGE are presented. This application of approximate reasoning based on linguistic modifiers gives satisfactory results. Besides, it provides a comfortable linguistic interpretation to the human mind thanks to the use of linguistic modifiers.

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