Fuzzy Granular Three-Way Concept-Cognitive Learning Approach with Weighted Pseudo-Concepts
LI Tailai1,2, YAN Mengyu1,2, LI Jinhai1,2
1. Yunnan Key Laboratory of Complex Systems and Brain-Inspired Intelligence, Kunming University of Science and Technology, Kunming 650500; 2. Faculty of Science, Kunming University of Science and Technology, Kunming 650500
Abstract：The integration of concept-cognitive learning and fuzzy three-way concepts is an important research direction in uncertain data analysis. However, existing methods still have limitations in the utilization of attribute information, the characterization of the information contributions of pseudo-concepts, and the efficiency of processing relatively large-scale data. To address these issues, a fuzzy granular three-way concept-cognitive learning approach with weighted pseudo-concepts(FG3WCCL) is proposed. First, attribute granulation is introduced, fuzzy three-way attribute granular concepts are defined, and a granular concept space is built. Then, the concepts are clustered according to the similarity of concept extensions. The weights are determined by jointly considering the information contributions of different concepts, the hierarchical decay factor, and hierarchical consistency. Weighted pseudo-concepts are consequently constructed. Furthermore, a spectral decomposition-based object reduction method is proposed. Representative objects are selected through hybrid sampling to reduce the modeling cost. Finally, comparative experiments on 14 datasets show that FG3WCCL achieves high classification accuracy.
[1] Guo D D, Xu W H, Ding W P,et al. Concept-cognitive learning survey: mining and fusing knowledge from data[J/OL]. Information Fusion, 2024, 109. https://doi.org/10.1016/j.inffus.2024.102426. [2] Zhang X Y, Guo D D, Xu W H.Two-way concept-cognitive lear-ning with multi-source fuzzy context[J]. Cognitive Computation, 2023, 15(5): 1526-1548. [3] Yao Y Y.Three-way granular computing, rough sets, and formal concept analysis[J]. International Journal of Approximate Reaso-ning, 2020, 116(C): 106-125. [4] Mi Y L, Shi Y, Li J H, et al. Fuzzy-based concept learning me-thod: exploiting data with fuzzy conceptual clustering[J]. IEEE Transactions on Cybernetics, 2022, 52(1): 582-593. [5] Deng X Y, Li J H, Qian Y H, et al. An emerging incremental fuzzy concept-cognitive learning model based on granular computing and conceptual knowledge clustering[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(3): 2417-2432. [6] Yuan K H, Xu W H, Li W T, et al. An incremental learning me-chanism for object classification based on progressive fuzzy three-way concept[J]. Information Sciences, 2022, 584: 127-147. [7] Guo D D, Xu W H, Qian Y H, et al. Fuzzy-granular concept-cognitive learning via three-way decision: performance evaluation on dynamic knowledge discovery[J]. IEEE Transactions on Fuzzy Systems, 2024, 32(3): 1409-1423. [8] Zhang C L,Tsang E C C, Xu W H, et al. Incremental concept-cognitive learning approach for concept classification oriented to weighted fuzzy concepts[J/OL]. Knowledge-Based Systems, 2023, 260. https://doi.org/10.1016/j.knosys.2022.110093. [9] Zhang L F, Yan E L, Hao T Y.Fuzzy concept-cognitive learning based on attention mechanism and three-way partial order structure[J/OL]. Applied Soft Computing, 2026, 191. https://doi.org/10.1016/j.asoc.2026.114664. [10] Guo D D, Xu W H, Qian Y H,et al. M-FCCL: memory-based con-cept-cognitive learning for dynamic fuzzy data classification and knowledge fusion[J/OL]. Information Fusion, 2023, 100. https://doi.org/10.1016/j.inffus.2023.101962. [11] DING Y, XU W H.Multi-granularity interval-intent fuzzy concept-cognitive learning: an attention-enhanced adaptive clustering frame-work[J/OL]. Information Fusion, 2026, 127(A). https://doi.org/10.1016/j.inffus.2025.103715. [12] XU W H, ZHANG C Z.RF-WFCCL: a random forest-driven weighted fuzzy concept cognitive learning[J/OL]. Fuzzy Sets and Systems, 2026, 529. https://doi.org/10.1016/j.fss.2025.109724. [13] Xie J P, Yang J, Li J H,et al. An efficient method of renewing object-induced three-way concept lattices involving decreasing attribute-granularity levels[J/OL]. International Journal of Appro-ximate Reasoning, 2024, 164. https://doi.org/10.1016/j.ijar.2023.109083. [14] Qi J J, Wei L, Yao Y Y.Three-way formal concept analysis[C]//Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology. Berlin, Germany: Springer, 2014: 732-741. [15] Wille R.Restructuring lattice theory: an approach based on hierarchies of concepts[C]//Proceedings of the 7th International Confe-rence on Formal Concept Analysis. Berlin, Germany: Springer, 2009: 314-339. [16] Ganter B, Stumme G, Wille R.Formal concept analysis: foundations and applications[M]. Berlin, Germany: Springer, 2005. [17] Zadeh L A.Fuzzy sets and information granularity[M]. Singapore, Singapore: World Scientific, 1996. [18] Zhang W X, Ma J M, Fan S Q.Variable threshold concept lattices[J]. Information Sciences, 2007, 177(22): 4883-4892. [19] Bělohlávek R, Sklenář V, Zacpal J.Crisply generated fuzzy concepts[C]//Proceedings of the 3rd International Conference on Formal Concept Analysis. Berlin, Germany: Springer, 2005: 269-284. [20] 龙柄翰,徐伟华.模糊三支概念分析与模糊三支概念格[J]. 南京大学学报(自然科学版), 2019, 55(4): 537-545. (Long B H, Xu W H.Fuzzy three-way concept analysis and fuzzy three-way concept lattice[J]. Journal of Nanjing University(Na-tural Science), 2019, 55(4): 537-545.) [21] Shi Y, Mi Y L, Li J H, et al. Concept-cognitive learning model for incremental concept learning[J]. IEEE Transactions on Systems, Man, and Cybernetics(Systems), 2021, 51(2): 809-821. [22] Mahoney M W, Drineas P.CUR matrix decompositions for improved data analysis[J]. PNAS, 2009, 106(3): 697-702. [23] Sassi M, Touzi A G, Ounelli H.Clustering quality evaluation based on fuzzy FCA[C]//Proceedings of the 18th International Conference on Database and Expert Systems Applications. Berlin, Germany: Springer, 2007: 639-649. [24] Niu J J, Chen D G, Li J H, et al. Fuzzy rule-based classification method for incremental rule learning[J]. IEEE Transactions on Fuzzy Systems, 2022, 30(9): 3748-3761. [25] Xu W H, Chen Y Q.Multi-attention concept-cognitive learning model: a perspective from conceptual clustering[J/OL]. Know-ledge-Based Systems, 2022, 252. https://doi.org/10.1016/j.knosys.2022.109472. [26] Zhai J H, Li N, Zhai M Y.The condensed fuzzy k-nearest neighbor rule based on sample fuzzy entropy[C]//Proceedings of the In-ternational Conference on Machine Learning and Cybernetics. Washington, USA: IEEE, 2011: 282-286. [27] Sarkar M.Fuzzy-rough nearest neighbor algorithms in classification[J]. Fuzzy Sets and Systems, 2007, 158(19): 2134-2152. [28] Keller J M, Gray M R, Givens J A. A fuzzy k-nearest neighbor algorithm[J]. IEEE Transactions on Systems, Man,Cybernetics, 1985, SMC-15(4): 580-585.
2026, Vol. 39 
