Purpose

The purpose of this paper is to design an inference engine to measure the level of readiness of each bank before starting the corporate sustainability auditing process. Based on the output of the designed inference engine, the audition team can decide about the audition resources and the auditing process.

Design/methodology/approach

In this paper, the hybrid rough and grey set theory are used to design and create a rule model system to measure the sustainability level of banks. First, 16 rule models are extracted using rough set theory (RST), and the cross-validation of each model is done. Then, the grey clustering is used to combine the same condition attributes and improve the validity of the final model. A total of 16 new rule models are extracted based on the decreased condition attributes, and the best model is selected based on the cross-validation results.

Findings

By comparing the accuracy of rough-gray’s rule models and as a result of decreasing the condition attributes, a proper increase in the accuracy of all models is obtained. Finally, the Naive/Genetic/object-related reducts model with 95.6% accuracy is selected as an inference engine to measure new banks’ readiness level.

Originality/value

Sustainability measurement of banks based on RST is a new approach in the field of corporate sustainability. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

中文翻译：

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基于粗糙灰色集理论的企业可持续发展评估

目的

本文的目的是设计一个推理引擎来衡量每家银行在开始企业可持续发展审计流程之前的准备程度。根据设计的推理引擎的输出，试听团队可以决定试听资源和审计过程。

设计/方法/方法

本文采用混合粗糙集和灰色集理论设计和创建了衡量银行可持续发展水平的规则模型系统。首先，使用粗糙集理论（RST）提取16个规则模型，并对每个模型进行交叉验证。然后，利用灰色聚类将相同的条件属性结合起来，提高最终模型的有效性。根据减少的条件属性，共提取出16个新的规则模型，并根据交叉验证结果选择最佳模型。

发现

通过比较粗灰规则模型的准确率，通过降低条件属性，得到所有模型准确率的适当提高。最后，选择准确率达到 95.6% 的 Naive/Genetic/object-related reduces 模型作为推理引擎来衡量新银行的准备程度。

原创性/价值

基于RST的银行可持续发展测量是企业可持续发展领域的一种新方法。此外，使用灰色聚类来组合条件属性是提高规则模型准确性的一种新解决方案。