Fuzzy-Granular Concept-Cognitive Learning via Three-Way Decision: Performance Evaluation on Dynamic Knowledge Discovery

Concept-cognitive learning (CCL) and three-way decision (3WD) models provide powerful techniques for knowledge discovery. Some early attempts in the field have successfully combined CCL and 3WD, i.e., three-way concept learning. However, only a few attempts were made to combine CCL with 3WD in a dynamic fuzzy context due to two challenges: 1) Three-way CCL incapability; 2) The current incremental three-way concept learning mechanism is insufficient to model real-time updating cognitive procedure. Hence, this article first shows some new standpoints on improving fuzzy-based CCL accuracy and then proposes fuzzy-granular three-way concept-cognitive learning (F3WG-CCL) for concept modeling and dynamic knowledge learning. Specifically, we first define a new F3WG-concept to characterize the knowledge embedded in fuzzy data. Furthermore, a big concept priority principle and an update mechanism are borrowed for concept recognition and dynamic concept cognition. Finally, we show that F3WG-CCL can be implemented simultaneously via theoretical guarantee and sufficient experimental, including 1) achieving state-of-the-art dynamic knowledge learning; 2) demonstrating that the three-way concept is effective in a fuzzy context; and 3) discovering that the big concept is valuable for fuzzy concept recognition. Our work will provide a powerful approach to research fuzzy-based CCL and dynamic knowledge discovery.