Evidence Sets

I have been working in mathematical models of uncertainty such as Fuzzy Set Theory and the Dempster-Shafer Theory of Evidence (DST). In particular, I developed a set structure named Evidence Sets, which extended Fuzzy Sets with the DST. Evidence sets were developed to address the shortcomings of fuzzy sets as models of linguistic/cognitive categories previously discussed by George Lakoff by providing a set structure capable of dealing better with the contextual nature of cognitive categories while preserving their prototypical effects as observed by Eleanor Rosch. To make evidence sets useful, I developed new measures of uncertainty for continuous domains, since, in their membership degrees, they capture three distinct types of uncertainty: fuzziness, nonspecificity and conflict. I have also used evidence sets and their measures of uncertainty to develop soft computing agents for a digital library and web tool named TalkMine, which is capable of adapting to different user personalities and learning new terms for existing documents. More information about evidence sets is available in a separate page. The figure below depicts a non-consonant evidence set.

Evidence Sets

Non-Consonant Evidence Set. The membership degree of an element in a set is defined by a set function known as a basic probability assignment. See details in [Rocha, 1999]

Project Members

Luis Rocha

Artemy Kolchinsky

Selected Project Publications