提出RPS源可靠性评估方法，提升模式识别准确率
Reliability Assessment of Information Sources Based on Random Permutation Set
- 基于排列顺序构建RPS到DST的转换方法
- 在多个数据集上实现更高分类准确率
- 适合处理带顺序信息的不确定性推理任务
在模式识别中，处理不确定性是影响决策与分类精度的关键挑战。证据理论（Dempster-Shafer Theory, DST）是应对不确定性的有效推理框架，而随机排列集（Random Permutation Set, RPS）通过考虑元素内部顺序，成为DST的更有序扩展。然而，目前缺乏RPS与DST之间的排列顺序转换方法，也缺少基于序列的概率转换机制。此外，RPS来源的可靠性问题亟待解决。为此，本文提出一种针对RPS的转换方法与概率转换策略，并基于此构建了基于概率转换的RPS源可靠性计算方法，应用于模式识别任务。实验结果表明，所提方法有效弥合了DST与RPS间的鸿沟，在分类任务中实现了更优的识别精度。
原文摘要 · Abstract (English)
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.
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