Abstract For set-valued information systems, there are many original dot-based approximation models based on tolerance relations and other developed tolerance relations. Because of the lack of efficient algorithms, they are not to accommodate the bigger and bigger set-valued information table. Therefore, it is a real challenge on how to efficiently calculate a high-quality approximation set in set-valued information systems. To address the challenge, we propose reliable approximation operators based on semi-monolayer covering for set-valued information systems. Benefiting from considerable research about tolerance rough set models and covering rough set models, the proposed approximation operators used a piecewise design to effectively reduce the negative effects of the set-valued records and provided high-quality approximation sets for set-valued information systems. Furthermore, the reliable semi-monolayer covering approximation sets are more easily granulated and efficiently calculated than before. Based on the equivalent granule-based forms, the corresponding granular algorithms are designed for the the improved approximation sets. The experiments on some UCI data sets show the improved approximation sets are high quality and efficient computational in set-valued information systems.

中文翻译：

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集值信息系统上的半单层覆盖粗糙集及其高效计算

摘要 对于集值信息系统，有许多原始的基于点的近似模型，它们基于容差关系和其他发达的容差关系。由于缺乏高效的算法，它们不能适应越来越大的集合值信息表。因此，如何有效地计算集值信息系统中的高质量近似集是一个真正的挑战。为了应对这一挑战，我们提出了基于半单层覆盖的可靠逼近算子，用于设置值信息系统。受益于对容差粗糙集模型和覆盖粗糙集模型的大量研究，所提出的近似算子采用分段设计，有效地减少了集值记录的负面影响，并为集值信息系统提供了高质量的近似集。此外，可靠的半单层覆盖近似集比以前更容易粒化和有效计算。在等价的基于粒形式的基础上，为改进的近似集设计了相应的粒算法。在一些 UCI 数据集上的实验表明，改进的近似集在集值信息系统中具有高质量和高效的计算能力。针对改进的近似集设计了相应的粒算法。在一些 UCI 数据集上的实验表明，改进的近似集在集值信息系统中具有高质量和高效的计算能力。针对改进的近似集设计了相应的粒算法。在一些 UCI 数据集上的实验表明，改进的近似集在集值信息系统中具有高质量和高效的计算能力。