A Novel Concept-Cognitive Learning Model Oriented to Three-Way Concept for Knowledge Acquisition

Abstract

Concept-cognitive learning (CCL) is the process of enabling machines to simulate the concept learning of the human brain. Existing CCL models focus on formal context while neglecting the importance of skill context. Furthermore, CCL models, which solely focus on positive information, restrict the learning capacity by neglecting negative information, and greatly impeding the acquisition of knowledge. To overcome these issues, we proposes a novel concept-cognitive learning model oriented to three-way concept for knowledge acquisition. First, this paper explains and investigates the relationship between skills and knowledge based on the three-way concept and its properties. Then, in order to simultaneously consider positive and negative information, describe more detailed information, learn more skills, and acquire accurate knowledge, a three-way information granule is described from the perspective of cognitive learning. Then, a transformation method is proposed to transform between different three-way information granules, allowing for the transformation of arbitrary three-way information granule into necessary, sufficient, sufficient and necessary three-way information granules. Finally, algorithm corresponding to the transformation method is designed, and subsequently tested across diverse UCI datasets. The experimental outcomes affirm the effectiveness and excellence of the suggested model and algorithm.