In this study, a new two-stage learning based reduction approach for fuzzy cognitive maps (FCM) is introduced in order to reduce the number of concepts. FCM is a graphical modeling technique that follows a reasoning approach similar to the human reasoning and the decision-making process. The FCM model incorporates the available knowledge and expertise in the form of concepts and in the direction and strength of the interactions among concepts. One of the modeling problems of FCMs is that oversized FCM models suffer from interpretability problems. An oversized FCM may contain concepts that are semantically similar and affect the other concepts in a similar way. This new study introduces a two-stage model reduction approach, and both static and dynamic analyses are considered without losing essential information. In the first stage, the number of concepts is reduced by merging similar concepts into clusters, whereas in the second stage the transformation function parameters of concepts are optimized. In order to show the benefit of using the proposed reduction approach, two sets of studies are conducted. First, a huge set of synthetic FCMs are generated, and the results of these statistical analyses are presented via various tables and figures. Subsequently, suggestions to the decision makers are given. Second, experimental studies are also presented to show the decision parameters and procedure for the proposed approach. The results show that after using the concept reduction approach presented in this study, the interpretability of FCM increases with an acceptable amount of information loss in the output concepts.

中文翻译：

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基于两阶段学习的模糊认知图缩减方法


在本研究中，引入了一种新的基于两阶段学习的模糊认知图（FCM）缩减方法，以减少概念的数量。 FCM 是一种图形建模技术，遵循类似于人类推理和决策过程的推理方法。 FCM 模型以概念的形式以及概念之间相互作用的方向和强度结合了可用的知识和专业知识。 FCM 的建模问题之一是过大的 FCM 模型会遇到可解释性问题。过大的 FCM 可能包含语义相似的概念，并以类似的方式影响其他概念。这项新研究引入了两阶段模型简化方法，在不丢失基本信息的情况下考虑了静态和动态分析。在第一阶段，通过将相似的概念合并成簇来减少概念的数量，而在第二阶段，优化概念的变换函数参数。为了展示使用所提出的减少方法的好处，进行了两组研究。首先，生成大量合成 FCM，并通过各种表格和图形呈现这些统计分析的结果。随后，向决策者提出建议。其次，还提出了实验研究来展示所提出方法的决策参数和程序。结果表明，使用本研究中提出的概念约简方法后，FCM 的可解释性随着输出概念中可接受的信息丢失量而增加。