Fuzzy Rule-Based Classification Method for Incremental Rule Learning
Fuzzy Rule-Based Classification Method for Incremental Rule Learning
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DOI：
10.1109/tfuzz.2021.3128061
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发表时间：
2022-09
影响因子：
11.9
通讯作者：
Jiaojiao Niu;De-gang Chen;Jinhai Li;Hui Wang
中科院分区：
文献类型：
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作者：
Jiaojiao Niu;De-gang Chen;Jinhai Li;Hui Wang
粒子已被广泛用于模糊数据集中的分类，以促进人工智能的发展。但是，由于数据类型的多样性，如何提高提取的颗粒规则的可读性，同时确保效率始终是一个挑战。由于颗粒计算（GRC）中的颗粒状还原可以简化真正的复杂问题和数据集，因此本文通过采用正式概念分析（FCA）基于GRC的GRC方法作为框架，从颗粒还原的角度进行颗粒状规则学习。具体而言，为了完成分类任务，我们首先提出了一种更新颗粒状还原的方法，然后在减少的数据集中探索模糊颗粒规则的更新机制。其次，为模糊的粒状规则学习提出了一种新型基于模糊规则的分类模型。为了验证提出的模型的有效性，进行了一些用于增量学习和模糊规则挖掘的数值实验，以证明FRCM可以实现最新的分类性能。
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.