A generalized concept-cognitive learning: A machine learning viewpoint
This work addresses a theoretical gap in concept-cognitive learning for researchers in formal concept analysis, granular computing, and cognitive computing, but it appears incremental as it generalizes existing frameworks.
The paper tackles the unclear relationships among cognitive computing, concept-cognitive computing, concept-cognitive learning, and concept-cognitive learning models by proposing a generalized concept-cognitive learning approach from a machine learning perspective, and experiments on datasets verify its feasibility for concept formation and cognitive processes.
Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL.