Purpose

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).

Design/methodology/approach

For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.

Findings

The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.

Practical implications

In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.

Originality/value

This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.

中文翻译：

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使用基于非相加灰色关联分析和DEMATEL的公差粗糙集进行模式分类

目的

为基于公差粗糙集的分类器（TRSC）推导适当的公差关系是一项挑战。传统的公差粗糙集采用简单的距离函数来确定公差关系。但是，这种简单的功能没有考虑标准权重和标准之间的相互作用。此外，传统的公差关系忽略了与模式之间直接和间接影响有关的相互依赖性。这项研究旨在将相互作用和相互依赖性纳入公差关系中，以开发非加性灰色TRSC（NG-TRSC）。

设计/方法/方法

对于模式分类，本研究应用了非加性灰色关联分析（GRA）和决策试验与评估实验室（DEMATEL）技术分别解决了相互作用和相互依赖性所引起的问题。

发现

将拟议的NG-TRSC得出的分类准确率与其他具有鲜明特征的TRSC进行了比较。结果表明，提出的分类器优于所考虑的其他TRSC。

实际影响

除了模式分类，建议的非加性灰色DEMATEL可以进一步简化管理决策者的操作并增强DEMATEL的适用性，从而进一步有利于管理决策的应用。

创意/价值

本文提出了用于模型分类的非累加灰度公差粗糙集（NG-TRS），为该领域做出了贡献。可以通过使用遗传算法确定相关参数，将非可累加的GRA与DEMATEL集成来构造所提出的NG-TRSC。