Linguistic Rule Induction Based on a Random Set Semantics

نویسندگان

  • Zengchang Qin
  • Jonathan Lawry
چکیده

Fuzzy logic based methods have been widely used in linguistic modeling [6]. Here we use a different framework of random set to interpret imprecise concepts. This framework is referred to as label semantics [8]. Within this framework, fuzzy concepts are modeled by quantifying the subjective uncertainty associated with whether or not a label expression is appropriate to describe a particular value. In this paper, a method of modeling data with logical expressions of fuzzy labels is discussed and a simple information based algorithm based on FOIL [10] is proposed for generating a set of linguistic rules for classification.

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تاریخ انتشار 2005