All Outputs (131)

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.

Explain the world – Using causality to facilitate better rules for fuzzy systems (2024)
Journal Article
Zhang, T., Wagner, C., & Garibaldi, J. M. (2024). Explain the world – Using causality to facilitate better rules for fuzzy systems. IEEE Transactions on Fuzzy Systems, 1-14. https://doi.org/10.1109/TFUZZ.2024.3457962

The rules of a rule-based system provide explanations for its behaviour by revealing the relationships between the variables captured. However, ideally, we have AI systems which go beyond explainable AI (XAI), that is, systems which not only explain... Read More about Explain the world – Using causality to facilitate better rules for fuzzy systems.

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.

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem (2024)
Journal Article
Lin, B., Li, J., Cui, T., Jin, H., Bai, R., Qu, R., & Garibaldi, J. (2024). A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem. Expert Systems with Applications, 249, Article 123515. https://doi.org/10.1016/j.eswa.2024.123515

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern... Read More about A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem.

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.

Towards Causal Fuzzy System Rules Using Causal Direction (2023)
Presentation / Conference Contribution
Zhang, T., Ying, J., Wagner, C., & Garibaldi, J. (2023, August). Towards Causal Fuzzy System Rules Using Causal Direction. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Incheon, Korea

Generating (fuzzy) rule bases from data can provide a rapid pathway to constructing (fuzzy) systems. However, direct rule generation approaches tend to generate very large numbers of rules. One reason for this is that such techniques are not designed... Read More about Towards Causal Fuzzy System Rules Using Causal Direction.

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.

Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease (2022)
Journal Article
Gonem, S., Taylor, A., Figueredo, G., Forster, S., Quinlan, P., Garibaldi, J. M., McKeever, T. M., & Shaw, D. (2022). Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease. Respiratory Research, 23, Article 203. https://doi.org/10.1186/s12931-022-02130-6

Background: The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop... Read More about Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease.

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.

Counterfactual rule generation for fuzzy rule-based classification systems (2022)
Presentation / Conference Contribution
Zhang, T., Wagner, C., & Garibaldi, J. M. (2022, July). Counterfactual rule generation for fuzzy rule-based classification systems. Presented at 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Padua, Italy

EXplainable Artificial Intelligence (XAI) is of in-creasing importance as researchers and practitioners seek better transparency and verifiability of AI systems. Mamdani fuzzy systems can provide explanations based on their linguistic rules, and thus... Read More about Counterfactual rule generation for fuzzy rule-based classification systems.

LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint (2022)
Journal Article
Jafari, M., Francis, S., Garibaldi, J. M., & Chen, X. (2022). LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint. Medical Image Analysis, 81, Article 102536. https://doi.org/10.1016/j.media.2022.102536

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the... Read More about LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint.

Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images (2022)
Journal Article
Li, Q., Li, B., Garibaldi, J. M., & Qiu, G. (2022). Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images. Remote Sensing, 14(14), Article 3361. https://doi.org/10.3390/rs14143361

In supervised deep learning, learning good representations for remote-sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self-supervised learning sho... Read More about Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images.

Lessons learned from the COVID-19 pandemic about sample access for research in the UK (2022)
Journal Article
Mai Sims, J., Lawrence, E., Glazer, K., Gander, A., Fuller, B., Garibaldi, J., Davidson, B., & Quinlan, P. R. (2022). Lessons learned from the COVID-19 pandemic about sample access for research in the UK. BMJ Open, 12(4), Article e047309. https://doi.org/10.1136/bmjopen-2020-047309

Objective Annotated clinical samples taken from patients are a foundation of translational medical research and give mechanistic insight into drug trials. Prior research by the Tissue Directory and Coordination Centre (TDCC) indicated that researcher... Read More about Lessons learned from the COVID-19 pandemic about sample access for research in the UK.

Extension of Restricted Equivalence Functions and Similarity Measures for Type-2 Fuzzy Sets (2021)
Journal Article
De Miguel, L., Santiago, R., Wagner, C., Garibaldi, J. M., Takac, Z., de Hierro, A. F. R. L., & Bustince, H. (2022). Extension of Restricted Equivalence Functions and Similarity Measures for Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 30(9), 4005-4016. https://doi.org/10.1109/tfuzz.2021.3136349

In this work, we generalize the notion of restricted equivalence function for type-2 fuzzy sets, leading to the notion of extended restricted equivalence functions. We also study how under suitable conditions, these new functions recover the standard... Read More about Extension of Restricted Equivalence Functions and Similarity Measures for Type-2 Fuzzy Sets.

A Constrained Parametric Approach for Modeling Uncertain Data (2021)
Journal Article
D'Alterio, P., Garibaldi, J., & Wagner, C. (2022). A Constrained Parametric Approach for Modeling Uncertain Data. IEEE Transactions on Fuzzy Systems, 30(9), 3967-3978. https://doi.org/10.1109/tfuzz.2021.3134797

Data obtained from the real-world tends to be uncertain: Measurement inaccuracies, variability in opinions, and human errors are just some of the reasons that make the information collection process noisy. In recent years, fuzzy sets have been used t... Read More about A Constrained Parametric Approach for Modeling Uncertain Data.

Network Intrusion Detection Based on Dynamic Intuitionistic Fuzzy Sets (2021)
Journal Article
Xie, J., Wang, H., Garibaldi, J. M., & Wu, D. (2022). Network Intrusion Detection Based on Dynamic Intuitionistic Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 30(9), 3460-3472. https://doi.org/10.1109/tfuzz.2021.3117441

Network security requires effective detection and proper analysis of abnormal network behavior. To address the uncertainty associated with the process of network intrusion detection, this article proposes a network intrusion-detection algorithm based... Read More about Network Intrusion Detection Based on Dynamic Intuitionistic Fuzzy Sets.