Outputs (25)

Incomplete Multi-view Data Learning via Adaptive Embedding and Partial l 2,1 Norm Constraints for Parkinson's Disease Diagnosis (2025)
Journal Article
Huang, Z., Wang, K., Chen, C., Chen, J., Wan, J., Yang, Z., Zhou, R., & Gan, H. (in press). Incomplete Multi-view Data Learning via Adaptive Embedding and Partial l 2,1 Norm Constraints for Parkinson's Disease Diagnosis. IEEE Journal of Biomedical and Health Informatics,

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by mental abnormalities and motor dysfunction. Its early classification and prediction of clinical scores have been major concerns for researchers. Currently, multi-vi... Read More about Incomplete Multi-view Data Learning via Adaptive Embedding and Partial l 2,1 Norm Constraints for Parkinson's Disease Diagnosis.

Towards More Flexible Fuzzy Membership Functions: Learning from Data (2025)
Presentation / Conference Contribution
Abbasov, F., Chen, C., & Garibaldi, J. M. (2025, July). Towards More Flexible Fuzzy Membership Functions: Learning from Data. Presented at 2025 IEEE International Conference on Fuzzy Systems, Reims, France

Fuzzy systems are widely recognised for their ability to model uncertainty and linguistic knowledge, but their effectiveness often depends on the choice of membership functions. Traditional approaches have relied on membership functions with predefin... Read More about Towards More Flexible Fuzzy Membership Functions: Learning from Data.

Fuzzy-Based Ensemble Method for Robust Concept Drift Detection in Multivariate Time Series (2025)
Presentation / Conference Contribution
Tavares, L. G., Lima, J., Melo, M., Chen, C., Garibaldi, J. M., Scatena, G. D. S., Costa, A. H. R., Gomi, E. S., Salles, R., Pacitti, E., Santos, I., Siqueira, I. G., Carvalho, D., Coutinho, R., Porto, F., & Ogasawara, E. (2025, June). Fuzzy-Based Ensemble Method for Robust Concept Drift Detection in Multivariate Time Series. Presented at International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy

Concept drift detection (CDD) is the general problem of identifying significant changes in streaming data distribution over time. Effective drift detection is important in industrial processes such as oil and gas exploration to mitigate financial los... Read More about Fuzzy-Based Ensemble Method for Robust Concept Drift Detection in Multivariate Time Series.

SoftED: Metrics for soft evaluation of time series event detection (2024)
Journal Article
Salles, R., Lima, J., Reis, M., Coutinho, R., Pacitti, E., Masseglia, F., Akbarinia, R., Chen, C., Garibaldi, J., Porto, F., & Ogasawara, E. (2024). SoftED: Metrics for soft evaluation of time series event detection. Computers and Industrial Engineering, 198, Article 110728. https://doi.org/10.1016/j.cie.2024.110728

Time series event detectors are evaluated mainly by standard classification metrics, focusing solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring dete... Read More about SoftED: Metrics for soft evaluation of time series event detection.

Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case (2024)
Preprint / Working Paper
Chen, C., Wagner, C., & Garibaldi, J. M. (2024). Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case

Since their introduction, fuzzy sets and systems have become an important area of research known for its versatility in modelling, knowledge representation and reasoning, and increasingly its potential within the context explainable AI. While the app... Read More about Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case.

Boundary-wise loss for medical image segmentation based on fuzzy rough sets (2024)
Journal Article
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2024). Boundary-wise loss for medical image segmentation based on fuzzy rough sets. Information Sciences, 661, Article 120183. https://doi.org/10.1016/j.ins.2024.120183

The loss function plays an important role in deep learning models as it determines the model convergence behavior and performance. In semantic segmentation, many methods utilize pixel-wise (e.g. cross-entropy) and region-wise (e.g. dice) losses while... Read More about Boundary-wise loss for medical image segmentation based on fuzzy rough sets.

Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation (2023)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., Pekaslan, D., & Garibaldi, J. M. (2023, August). Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Songdo Incheon, Korea

Deep learning models have achieved high performance in numerous semantic segmentation tasks. However, when the input data at test time do not resemble the training data, deep learning models can not handle them properly and will probably produce poor... Read More about Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation.

The Design and Implementation of a Constrained Interval Type-2 Fuzzy System for Credit Card Fraud Detection (2023)
Presentation / Conference Contribution
Wang, X., Li, M., Chen, C., & Garibaldi, J. M. (2023, August). The Design and Implementation of a Constrained Interval Type-2 Fuzzy System for Credit Card Fraud Detection. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Songdo Incheon, Korea

Fuzzy systems with type-1, interval type-2 and general type-2 fuzzy sets have been widely applied in various fields. Constrained Interval Type-2 (CIT2) fuzzy sets and systems are an approach designed to improve the interpretability of type-2 fuzzy in... Read More about The Design and Implementation of a Constrained Interval Type-2 Fuzzy System for Credit Card Fraud Detection.

A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty (2022)
Journal Article
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2022). A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty. IEEE Transactions on Fuzzy Systems, 31(8), 2532-2544. https://doi.org/10.1109/tfuzz.2022.3228332

Deep learning methods have achieved an excellent performance in medical image segmentation. However, the practical application of deep learning-based segmentation models is limited in clinical settings due to the lack of reliable information about th... Read More about A Novel Quality Control Algorithm for Medical Image Segmentation Based on Fuzzy Uncertainty.

Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement (2022)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2022, July). Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement. Presented at 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Padua, Italy

Deep convolutional neural networks (DCNN)-based methods have achieved promising performance in semantic image segmentation. However, in practical applications, it is important not only to produce the segmentation result but also to inform the segment... Read More about Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement.