Fuzzy Rule-Based Classification Method for Incremental Rule Learning

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