Abstract Three-way decisions are a generalization of classical decision theory and receive increasing attentions from various fields to handle decision-making problems, especially when involving in incomplete information. An interval is a typical notion of information representation with incompleteness and uncertainty. To measure the dominance degree of one interval dominating or being dominated by another is a hot issue. In this paper, a novel dominance measure is constituted by considering both lengths and locations of intervals. The proposed dominance measure distinguishes two overlapped or coincided intervals, and is able to quantize the separation degree of disjoint intervals. A notion of variable precision overall dominance relation is introduced by integrating both the attribute-wise evaluation information and overall dominance degree of objects. Based on the constituted dominance relation, two three-way decision models are presented in interval-valued decision systems with categorical and interval-valued decision attributes, respectively. Numerical examples of three-way decisions and simulated experiments on classification over two synthetic and four benchmark datasets are presented. Experimental results indicate that the proposed model can produce lower classification errors in comparison with existing methods.

中文翻译：

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区间值决策系统中的部分总体优势三向决策模型

摘要 三路决策是经典决策理论的概括，在处理决策问题，尤其是涉及不完全信息时，越来越受到各个领域的关注。区间是具有不完整性和不确定性的信息表示的典型概念。衡量一个区间支配或被另一个区间支配的支配程度是一个热点问题。在本文中，通过考虑间隔的长度和位置，构成了一种新的优势度量。所提出的优势度量区分了两个重叠或重合的区间，并且能够量化不相交区间的分离程度。通过整合对象的属性评估信息和整体优势度，引入了可变精度整体优势关系的概念。基于构成的优势关系，在区间值决策系统中分别给出了具有分类和区间值决策属性的两种三向决策模型。给出了对两个合成数据集和四个基准数据集进行分类的三向决策和模拟实验的数值示例。实验结果表明，与现有方法相比，所提出的模型可以产生更低的分类错误。给出了对两个合成数据集和四个基准数据集进行分类的三向决策和模拟实验的数值示例。实验结果表明，与现有方法相比，所提出的模型可以产生更低的分类错误。给出了对两个合成数据集和四个基准数据集进行分类的三向决策和模拟实验的数值示例。实验结果表明，与现有方法相比，所提出的模型可以产生更低的分类错误。