Evaluating Evidential Reliability In Pattern Recognition Based On Intuitionistic Fuzzy Sets
This addresses evidence reliability for classification in complex scenarios, but it is incremental as it builds on existing discounting methods.
The paper tackled the problem of determining evidence source reliability in Dempster-Shafer theory by combining it with Intuitionistic Fuzzy Sets to propose the Fuzzy Reliability Index algorithm, which enhanced reliability estimation and showed superiority in comparisons with existing methods.
Determining the reliability of evidence sources is a crucial topic in Dempster-Shafer theory (DST). Previous approaches have addressed high conflicts between evidence sources using discounting methods, but these methods may not ensure the high efficiency of classification models. In this paper, we consider the combination of DS theory and Intuitionistic Fuzzy Sets (IFS) and propose an algorithm for quantifying the reliability of evidence sources, called Fuzzy Reliability Index (FRI). The FRI algorithm is based on decision quantification rules derived from IFS, defining the contribution of different BPAs to correct decisions and deriving the evidential reliability from these contributions. The proposed method effectively enhances the rationality of reliability estimation for evidence sources, making it particularly suitable for classification decision problems in complex scenarios. Subsequent comparisons with DST-based algorithms and classical machine learning algorithms demonstrate the superiority and generalizability of the FRI algorithm. The FRI algorithm provides a new perspective for future decision probability conversion and reliability analysis of evidence sources.