Reliability Assessment of Information Sources Based on Random Permutation Set

TL;DR

This paper introduces a new method for assessing the reliability of information sources using Random Permutation Sets, enhancing uncertainty handling in pattern recognition and improving classification accuracy.

Contribution

It proposes a novel transformation and probability method for RPS, addressing the lack of existing techniques and enabling reliable source evaluation in DST extensions.

Findings

The approach effectively bridges DST and RPS.

It improves recognition accuracy in classification tasks.

Experimental results validate the method's effectiveness.

Abstract

In pattern recognition, handling uncertainty is a critical challenge that significantly affects decision-making and classification accuracy. Dempster-Shafer Theory (DST) is an effective reasoning framework for addressing uncertainty, and the Random Permutation Set (RPS) extends DST by additionally considering the internal order of elements, forming a more ordered extension of DST. However, there is a lack of a transformation method based on permutation order between RPS and DST, as well as a sequence-based probability transformation method for RPS. Moreover, the reliability of RPS sources remains an issue that requires attention. To address these challenges, this paper proposes an RPS transformation approach and a probability transformation method tailored for RPS. On this basis, a reliability computation method for RPS sources, based on the RPS probability transformation, is introduced and applied to pattern recognition. Experimental results demonstrate that the proposed approach effectively bridges the gap between DST and RPS and achieves superior recognition accuracy in classification problems.