期刊
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
卷 8, 期 1, 页码 75-94出版社
ATLANTIS PRESS
DOI: 10.1080/18756891.2014.964009
关键词
medical diagnosis; inclusion measure; Dempster-Shafer theory; uncertainty; evidential reasoning; Fuzzy sets
资金
- Program for Changjiang Scholars and Innovative Research Team in University [IRT1208]
For medical diagnosis, fuzzy Dempster-Shafer theory is extended to model domain knowledge under probabilistic and fuzzy uncertainty. However, there are some information loss using discrete fuzzy sets and traditional matching degree method. This study aims to provide a new evidential structure to reduce information loss. This paper proposes a new intuitionistic fuzzy evidential reasoning (IFER) approach which combines intuitionistic trapezoidal fuzzy numbers and inclusion measure to improve the accuracy of representation and reasoning. The proposed approach has been validated by a stroke diagnosis. It is shown that the IFER approach leads to more accurate results.
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