Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due to the diversity of data types, how to improve the readability of the extracted granular rules while ensuring efficiency is always a challenge. Since granular reduct in granular computing (GrC) can simplify real complex problem and dataset, this article carries out granular rule learning from the perspective of granular reduct by taking formal concept analysis (FCA)-based GrC method as a framework. Specifically, for achieving classification task, we first propose a method to update the granular reduct, and then explore the updating mechanism of fuzzy granular rule in a reduced dataset. Second, a novel fuzzy rule-based classification model named FRCM is presented for fuzzy granular rule learning. In order to verify the effectiveness of the proposed model, some numerical experiments for incremental learning and fuzzy rule mining are conducted to demonstrate that FRCM can achieve the state-of-the-art classification performance.

中文翻译：

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平衡群体决策共识与和谐的态度信任推荐机制


粒度规则已广泛用于模糊数据集的分类，以促进人工智能的进步。然而，由于数据类型的多样性，如何在保证效率的同时提高提取的粒度规则的可读性始终是一个挑战。由于粒度计算（GrC）中的粒度归约可以简化现实复杂问题和数据集，因此本文以基于形式概念分析（FCA）的GrC方法为框架，从粒度归约的角度进行粒度规则学习。具体来说，为了实现分类任务，我们首先提出一种更新粒度约简的方法，然后探索约简数据集中模糊粒度规则的更新机制。其次，提出了一种新颖的基于模糊规则的分类模型（FRCM），用于模糊粒度规则学习。为了验证所提出模型的有效性，进行了一些增量学习和模糊规则挖掘的数值实验，以证明 FRCM 可以实现最先进的分类性能。