With the development of society, data noise and other factors will cause the incompleteness of information systems. Objects may increase or decrease over time in information systems. The classical information system can be extended to the incomplete interval-valued decision information system (IIDIS) that is the researching object of this paper. Incremental learning technique is a significant method for solving approximate sets under dynamic data. This article defines a multi-threshold tolerance relation based on the set pair analysis theory and establishes a rough set model in IIDIS. Then, several methods and algorithms for statically/dynamically solving approximate sets are shown. Finally, comparative experiments from six UCI data sets show both dynamic algorithms take less time than the static algorithm to calculate the approximate sets no matter how object set changes.

中文翻译：

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不完整区间值决策信息系统中基于多阈值公差关系的动态更新近似

随着社会的发展，数据噪声等因素将导致信息系统的不完善。在信息系统中，对象可能随时间增加或减少。可以将经典信息系统扩展为不完整的区间值决策信息系统（IIDIS），这是本文的研究对象。增量学习技术是解决动态数据下近似集的一种重要方法。本文基于集对分析理论定义了一个多阈值公差关系，并在IIDIS中建立了一个粗糙集模型。然后，显示了用于静态/动态求解近似集的几种方法和算法。最后，