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

doi:10.1109/TFUZZ.2022.3216110

2023-06-01

PRE

DOI：10.1109/TFUZZ.2022.3216110

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摘要

En 中文

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: the sufficient and necessary granule concept is only obtained from the necessary granule or sufficient granule concept; and 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 article proposes a novel two-way CCL (TCCL) method for dynamic concept learning in a fuzzy context for these problems and challenges. Unlike TCL, fuzzy-based TCCL (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 CCL.

Keyword:

Granular computing

Object oriented modeling

Brain modeling

Systematics

Lattices

Context modeling

Computational modeling

Concept-cognitive learning (CCL)

formal concept analysis

fuzzy set

granular computing