Two-Way Concept-Cognitive Learning Method: A Fuzzy-Based Progressive Learning

Abstract

Granular computing (GrC) and two-way concept learning (TCL) are influential studies of knowledge processing and cognitive learning. A central notion of two-way concept learning is learning concepts from an arbitrary information granule. Although TCL has been widely adopted for concept learning and formal concept analysis in a fuzzy context, the existing studies of TCL still have some issues: 1) the sufficient and necessary granule concept is only obtained from the necessary granule or sufficient granule concept; 2) the cognitive mechanism ignores integrating past experiences into itself to deal with dynamic data. Meanwhile, concept-cognitive learning (CCL) method still faces challenges, such as incomplete cognitive and weak generalization ability. This paper proposes a novel two-way concept-cognitive learning method for dynamic concept learning in a fuzzy context for these problems and challenges. Unlike TCL, fuzzy-based twoway concept-cognitive learning (F-TCCL) is more flexible and less time-consuming to learn granule concepts from the given clue, and meanwhile, it is good at dynamic concept learning. Moreover, we design a fuzzy-based progressive learning mechanism within this framework under the dynamic environment. Some numerical experiments on public datasets verify the effectiveness of our proposed method. The considered framework can provide a convenient novel method for researching two-way learning and concept-cognitive learning.