Drawing reasonable conclusions from information under similarity modelled contexts
by Ronald R. Yager
International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS), Vol. 1, No. 1, 2009

Abstract: We are interested in the process of making reasonable conclusions about the value of a variable. We indicate that reasonableness generally depends on the information we have about the variable as well as the context in which we shall use the assumed value. In order to include a wide range of imprecise and uncertain information, we use granular computing technologies such as fuzzy sets, Dempster-Shafer belief structures and probability theory to represent our knowledge and conclusions. While context is a very diverse idea, in order to provide some structure, we restrict ourselves to the special case where context can be modelled using a similarity relationship. Within this framework, we suggest a measure of the reasonableness of drawing conclusions from information in the context of a similarity relationship. We look at the properties of this measure and investigate its performance in a number of special cases.

Online publication date: Wed, 24-Jun-2009

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