Concept learning based on improved FCM- BiLSTM for fuzzy data classification and fusion
Abstract Concept-Cognitive Learning (CCL) is an effective concept learning approach that simulates human cognitive processes to facilitate knowledge discovery. However, existing CCL methods face two significant challenges. One is that existing models often assume accurate labels and ignore the possi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-14821-3 |
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| Summary: | Abstract Concept-Cognitive Learning (CCL) is an effective concept learning approach that simulates human cognitive processes to facilitate knowledge discovery. However, existing CCL methods face two significant challenges. One is that existing models often assume accurate labels and ignore the possibility of inaccuracy, which may lead to decisions based on incorrect information. The other one is that the cognitive mechanisms in current CCL models do not account for the dependency relationships between objects, which limits the model’s ability to adapt to diverse datasets and handle complex relational patterns. Inspired by both fuzzy clustering and deep learning, this paper proposes a novel Concept-Cognitive Learning Model (FCLSCL), which integrates an improved Fuzzy C-means (FCM) and Bidirectional Long Short-Term Memory Network (BiLSTM). Specifically, an improved FCM is designed under the framework of fuzzy concept clustering to learn pseudo-concepts for each category and obtain the membership degree of each object to every category. Then, the weighted fuzzy concepts are introduced to capture the uncertainty of datasets by taking both membership degree and pseudo-concepts into account. For achieving concept classification tasks, the intents of the weighted fuzzy concept and fuzzy concept are concatenated together as inputs for BiLSTM to achieve bidirectional concept learning. Finally, experimental results, compared with several popular machine learning models, CCL methods and LSTM, demonstrate the effectiveness of the proposed FCLSCL. |
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| ISSN: | 2045-2322 |