[1]XU Weihua,ZHANG Chongze.Fuzzy concept cognitive learning based on multi-scale attention[J].CAAI Transactions on Intelligent Systems,2026,21(3):783-791.[doi:10.11992/tis.202510039]
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Fuzzy concept cognitive learning based on multi-scale attention

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 3 Page number: 783-791 Column: 学术论文—人工智能基础 Public date: 2026-05-05
Title:
Fuzzy concept cognitive learning based on multi-scale attention
Author(s):
XU Weihua;  ZHANG Chongze
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Keywords:
concept-cognitive learning; multi-scale; attention mechanism; granular computing; object classification; formal context; knowledge discovery; concept clustering
CLC:
TP18
DOI:
10.11992/tis.202510039
Abstract:
Concept-cognitive learning (CCL) treats concepts as the fundamental carriers of knowledge and examines the cognitive learning process of objects. It has been widely applied to knowledge discovery and object classification. However, existing fuzzy CCL models are mostly constructed at a single scale, which limits their ability to exploit multi-scale information and overlooks the role of attention mechanisms in human cognition. To address these issues, this paper proposes a multi-scale attention-based fuzzy concept-cognitive learning (MSA-CCL) model. The proposed method first constructs multi-scale fuzzy formal contexts and selects the optimal scale through consistency evaluation for fuzzy concept learning. Next, an attention mechanism is introduced for each conditional attribute to build a fuzzy concept attention space, which highlights the importance of key attributes. Finally, a pseudo-fuzzy concept attention space is generated to perform object classification and concept recognition based on the similarity between new objects and pseudo-concepts. Experiments on nine UCI machine learning repository datasets demonstrate the effectiveness and feasibility of the proposed method.
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