Incremental concept cognitive learning in dynamic formal contexts based on attribute partial order structure diagram

Partial order formal structure analysis (POFSA) is an emerging theory in the field of concept cognitive learning (CCL). Attribute partial order structure diagram (APOSD) is the visual expression of the knowledge structure in POFSA. It has the advantages of explicit expression of the hierarchies of a...

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Bibliographic Details
Published in:Computational & applied mathematics 2024-09, Vol.43 (6), Article 319
Main Authors: Ren, Yunli, Zhang, Yunxia, Hong, Wenxue
Format: Article
Language:English
Subjects:
Applications of Mathematics
Computational Mathematics and Numerical Analysis
Hierarchies
Knowledge bases (artificial intelligence)
Learning
Mathematical Applications in Computer Science
Mathematical Applications in the Physical Sciences
Mathematics
Mathematics and Statistics
Nodes
Position (location)
Structural analysis
Teaching methods
Citations: Items that this one cites
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Summary:Partial order formal structure analysis (POFSA) is an emerging theory in the field of concept cognitive learning (CCL). Attribute partial order structure diagram (APOSD) is the visual expression of the knowledge structure in POFSA. It has the advantages of explicit expression of the hierarchies of attributes and concise visual expression of the knowledge structure. This paper mainly focuses on the incremental CCL of APOSD in dynamic data circumstances. Firstly, the concept of location information coding of nodes in APOSD is proposed to express the position of nodes in the entire diagram as well as the relationships between nodes, which is an important tool throughout this paper. Secondly, by analyzing the relationship between new objects and objects in the original diagram, dynamic learning strategy for APOSD is proposed. Thirdly, in order to balance the efficiency and accuracy of dynamic learning, a dynamic-static alternating self-learning method for APOSD is proposed, which is an improved incremental learning strategy. Finally, comparative experiments illustrate that compared with non-incremental learning method of APOSD and concept lattice, the two proposed incremental learning methods of APOSD can effectively achieve dynamic self-updating of the knowledge base when processing dynamic data, and provide another perspective for discovering knowledge from the same data. Besides, the effectiveness of the improved incremental learning strategy is verified as well.
ISSN:2238-3603
1807-0302
DOI:10.1007/s40314-024-02826-z