Fuzzy concept cognitive learning based on knowledge space theory

Ju Huang; Yidong Lin; Wen Sun; Ju Huang; Yidong Lin; Wen Sun

doi:10.3934/math.2025985

AIMS Mathematics

2025, Volume 10, Issue 9: 22127-22149. doi: 10.3934/math.2025985

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Research article

Fuzzy concept cognitive learning based on knowledge space theory

1.
School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, China
2.
Fujian Key Laboratory of Granular Computing and Applications, Minnan Normal University, Zhangzhou 363000, China

Received: 05 July 2025 Revised: 05 September 2025 Accepted: 08 September 2025 Published: 24 September 2025
MSC : 03B52, 03E72

Concept cognitive learning (CCL) constitutes a rigorous and current cognitive theory for the representation and learning of concepts of the human brain. The well-established CCL models pay close attention to construct a concept space, in which all the attributes are mastered concurrently. However, few attempts have been made to combine CCL with cognitive logic in a fuzzy context due to attribute precedence. For this case, this paper first develops a cognitive surmise relationship among attributes, cognitive transitions, and discrimination of fuzzy concepts based on knowledge space theory. Furthermore, utilizing the inherent information of concepts, the attributes are weighted to accurately understand and apply fuzzy concepts. To better derive benefits from the fuzziness and uncertainty of knowledge, an approach is provided to improve performance through the fusion of fuzzy concepts. Empirical studies on twenty datasets reveal the effectiveness and efficiency of the proposed model.
- concept cognitive learning,
- concept fusion,
- fuzzy concept,
- knowledge space theory
Citation: Ju Huang, Yidong Lin, Wen Sun. Fuzzy concept cognitive learning based on knowledge space theory[J]. AIMS Mathematics, 2025, 10(9): 22127-22149. doi: 10.3934/math.2025985

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Abstract

Concept cognitive learning (CCL) constitutes a rigorous and current cognitive theory for the representation and learning of concepts of the human brain. The well-established CCL models pay close attention to construct a concept space, in which all the attributes are mastered concurrently. However, few attempts have been made to combine CCL with cognitive logic in a fuzzy context due to attribute precedence. For this case, this paper first develops a cognitive surmise relationship among attributes, cognitive transitions, and discrimination of fuzzy concepts based on knowledge space theory. Furthermore, utilizing the inherent information of concepts, the attributes are weighted to accurately understand and apply fuzzy concepts. To better derive benefits from the fuzziness and uncertainty of knowledge, an approach is provided to improve performance through the fusion of fuzzy concepts. Empirical studies on twenty datasets reveal the effectiveness and efficiency of the proposed model.

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