通过邻域与原集优化粗糙集近似空间，提升决策准确性。
Enhancement of Approximation Spaces by the Use of Primals and Neighborhood
- 引入邻域与原集构造四种新粗糙集模型
- 降低不确定区域，提升上下近似算子精度
- 适合处理不完整数据的决策分析场景
粗糙集理论是处理不完整信息的重要方法，通过等价关系划分全域并生成块。为增强灵活性和应用范围，本文提出四种受‘邻域与原集’启发的新广义粗糙集模型。这些模型旨在最小化不确定性区域，帮助决策者更有效地分析与评估数据。实验表明，相比现有方法，新模型在改进上下近似算子及准确率测量方面表现更优。模型保留了粗糙集的核心特性，如单调性，有助于评估数据不确定性并提升结果可信度。通过具体实例比较，验证了新方法在日常健康问题中的更高准确性。
原文摘要 · Abstract (English)
Rough set theory is one of the most widely used and significant approaches for handling incomplete information. It divides the universe in the beginning and uses equivalency relations to produce blocks. Numerous generalized rough set models have been put out and investigated in an effort to increase flexibility and extend the range of possible uses. We introduce four new generalized rough set models that draw inspiration from "neighborhoods and primals" in order to make a contribution to this topic. By minimizing the uncertainty regions, these models are intended to assist decision makers in more effectively analyzing and evaluating the provided data. We verify this goal by demonstrating that the existing models outperform certain current method approaches in terms of improving the approximation operators (upper and lower) and accuracy measurements. We claim that the current models can preserve nearly all significant aspects associated with the rough set model. Preserving the monotonic property, which enables us to assess data uncertainty and boost confidence in outcomes, is one of the intriguing characterizations derived from the existing models. With the aid of specific instances, we also compare the areas of the current approach. Finally, we demonstrate that the new strategy we define for our everyday health-related problem yields more accurate findings.
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