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| A Layer-by-Layer Attribute Reduction Algorithm for Fuzzy Linguistic Attribute Partial Order Structure Diagram |
| PANG Kuo1, ZHOU Ai1, YANG Xinran2, LI Nan2, ZOU Li3, LU Mingyu1 |
1. Information Science and Technology College, Dalian Maritime University, Dalian 116026;
2. School of Computer and Information Technology, Liaoning Normal University, Dalian 116081;
3. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250102 |
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Abstract As a data visualization tool, attribute partial order structure diagram can solve the problem of user cognitive overload in formal concept analysis effectively. People often express preference information through fuzzy linguistic values in real life,and thus a large amount of fuzzy linguistic-valued data is generated. To solve the problem of attribute reduction in fuzzy linguistic environment, a layer-by-layer attribute reduction algorithm for fuzzy linguistic attribute partial order structure diagram is proposed in this paper. Firstly, the fuzzy linguistic attribute partial order structure diagram is constructed based on the fuzzy linguistic-valued formal context, and the fuzzy linguistic-valued data is embedded into the attribute partial order structure diagram. The order relation and the incomparable relation between fuzzy linguistic values are expressed by the linguistic truth-valued lattice implication algebra acting as the representation model of the fuzzy linguistic values. Secondly, the fuzzy linguistic attribute partial order structure diagram is employed and the nodes that do not form edges with the underlying nodes are searched to obtain the minimum attribute subset with the fuzzy linguistic-valued formal context discrimination ability unchanged. On the premise of ensuring the class equivalence of the fuzzy linguistic attribute partial order structure diagram, the difference attribute between the node and its child nodes is calculated, and the corresponding layer-by-layer attribute reduction model is constructed. Finally, examples and comparative experiments verify the effectiveness and practicability of the proposed method.
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Received: 08 April 2022
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| Fund:Supported by National Natural Science Foundation of China(No.61976124) |
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Corresponding Authors:
LU Mingyu,Ph.D., professor. His research interests include data mining and natural language processing.
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About author:: PANG Kuo, Ph.D. candidate. His research interests include machine learning, intelligent information processing and formal concept analysis.
ZHOU Ai, Ph.D. candidate. Her research interests include natural language processing and author identification.
YANG Xinran, master student. Her research interests include intelligent information processing, multivalued logic and formal concept analysis.
LI Nan, master student. His research inte-rests include intelligent information processing and formal concept analysis.
ZOU Li, Ph.D., professor. Her research interests include data mining, intelligent information processing and formal concept ana-lysis. |
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2022, Vol. 35 
