How to translate words into numbers? a fuzzy approach for the numerical translation of verbal probabilities

Authors:
Franziska Bocklisch;Steffen F. Bocklisch;Josef F. Krems
Affiliations:
Chemnitz University of Technology, Cognitive and Engineering Psychology, Chemnitz, Germany;Chemnitz University of Technology, Systems Theory, Chemnitz, Germany;Chemnitz University of Technology, Cognitive and Engineering Psychology, Chemnitz, Germany
Venue:
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Year:
2010

Citing 4
Cited 1

Comparing the calibration and coherence of numerical and verbal probability judgments

Management Science
Probability theory and fuzzy logic are complementary rather than competitive

Technometrics
Computing with words in decision making: foundations, trends and prospects

Fuzzy Optimization and Decision Making
Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system

Artificial Intelligence in Medicine

How medical expertise influences the understanding of symptom intensities - a fuzzy approach

USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper describes a general two-step procedure for the numerical translation of linguistic terms using parametric fuzzy potential membership functions. In an empirical study 121 participants estimated numerical values that correspond to 13 verbal probability expressions. Among the estimates are the most typical numerical equivalent and the minimal and maximal values that just correspond to the given linguistic terms. These values serve as foundation for the proposed fuzzy approach. Positions and shapes of the resulting membership functions suggest that the verbal probability expressions are not distributed equidistantly along the probability scale and vary considerably in symmetry, vagueness and overlap. Therefore we recommend the proposed empirical procedure and fuzzy approach for future investigations and applications in the area of decision support.