R-FCCL: An Approach of Fuzzy-Based Concept-Cognitive Learning with Robustness for High-Dimensional Data

Guo Doudou; Xu Weihua

doi:10.7544/issn1000-1239.202330428

Journal of Computer Research and Development > 2025 > 62(2): 383-396. > DOI: 10.7544/issn1000-1239.202330428 CSTR: 32373.14.issn1000-1239.202330428

Guo Doudou, Xu Weihua. R-FCCL: An Approach of Fuzzy-Based Concept-Cognitive Learning with Robustness for High-Dimensional Data[J]. Journal of Computer Research and Development, 2025, 62(2): 383-396. DOI: 10.7544/issn1000-1239.202330428

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R-FCCL: An Approach of Fuzzy-Based Concept-Cognitive Learning with Robustness for High-Dimensional Data

Guo Doudou,
Xu Weihua^,

College of Artificial Intelligence, Southwest University, Chongqing 400715

Funds: This work was supported by the National Natural Science Foundation of China (62376229) and the Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0027).

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Author Bio:
Guo Doudou: born in 1995. PhD candidate, Member of CCF. His main research interests include concept-cognitive learning, granular computing, and machine learning

Xu Weihua: born in 1979. PhD, professor, PhD supervisor. His main research interests include granular computing, cognitive computing, data mining, and machine learning
Received Date: June 04, 2023
Revised Date: February 05, 2024
Available Online: September 04, 2024

Graphical Abstract

Abstract

Abstract

With the rapid development of global informationization, data mining and knowledge discovery of high-dimensional data have been a hotspot in the field of artificial intelligence and data science. However, the sparse sample and redundant feature issues of high-dimensional data make it challenging to ensure the generalization and interpretability of traditional statistical models and machine learning methods. Hence, we present fuzzy-based concept-cognitive learning with robustness for the imbalance problem between high-dimensional data and weak knowledge evolution ability. The main idea is to explore the knowledge structure and cognitive learning mechanism of high-dimensional data from the concept perspective. We propose a high-dimensional data classification method based on the concept-cognitive learning mechanism in the fuzzy formal context. Furthermore, the cognitive learning process of fuzzy concepts is described from two different perspectives by the positive and negative cognitive learning operators of fuzzy three-way concepts. Finally, the fusion of fuzzy three-way concepts completes the task of concept identification and data classification. Extensive experiments performed on 12 real data sets compared with 12 state-of-the-art classification methods also verify the robustness and effectiveness of the proposed method. The considered framework can provide a convenient novel tool for high-dimensional data knowledge discovery research and fuzzy-based concept-cognitive learning.
- formal concept analysis,
- concept-cognitive learning,
- granular computing,
- high-dimensional data,
- three-way decision

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References (47)

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