Information Fusion and Decision-Making Utilizing Additional Permutation Information

Meizhu Li, Linshan Li and Qi Zhang ()
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Meizhu Li: School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
Linshan Li: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Qi Zhang: School of Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Mathematics, 2024, vol. 12, issue 22, 1-15

Abstract: The theory of multi-source information fusion plays a pivotal role in decision-making, especially when handling uncertain or imprecise information. Among the existing frameworks, evidence theory has proven effective for integrating diverse information sources to support informed decision-making. Recently, Random Permutation Set Theory (RPST), an extension of evidence theory, has shown significant practical value due to its ability to leverage the additional information inherent in event permutations. This insight inspires the utilization of permutation data to enhance the decision-making process. When employing RPST for decision-making and fusion, the order in which the fusion is performed can substantially influence the final results. To address this issue, we propose a novel approach that utilizes Fisher Scores to extract additional permutation information to guide decision-making within the RPST framework. Experimental results on the Iris dataset validate the feasibility and effectiveness of the proposed method. Compared to fusion methods employing weighted averaging, our approach, which leverages additional information to determine the fusion order, demonstrates superior accuracy across various training set proportions, achieving an accuracy of 96.26% at an 80% training set proportion. This provides an enhanced strategy for decision-making under uncertainty.

Keywords: decision-making; evidence theory; random permutation set theory; Fisher scores (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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