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All Outputs (22)

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.

FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation (2021)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2021, July). FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation. Presented at IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Luxembourg, Luxembourg

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have c... Read More about FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation.

An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems (2021)
Presentation / Conference Contribution
Chen, C., Zhao, Y., Wagner, C., Pekaslan, D., & Garibaldi, J. M. (2021, July). An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems. Presented at 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg

Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition app... Read More about An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems.

Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox (2021)
Presentation / Conference Contribution
Razak, T. R., Chen, C., Garibaldi, J. M., & Wagner, C. (2021, July). Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox. Presented at 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, Luxembourg

The use of Hierarchical Fuzzy Systems (HFS) has been well acknowledged as a good approach in reducing the complexity and improving the interpretability of fuzzy logic systems (FLS). Over the past years, many fuzzy logic toolkits have been made availa... Read More about Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox.

Comparing Intervals Using Type Reduction (2020)
Presentation / Conference Contribution
Runkler, T. A., Chen, C., Coupland, S., & John, R. (2020, July). Comparing Intervals Using Type Reduction. Presented at 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK

Many decision making processes are based on choosing options with maximum utility. Often utility assessments are associated with uncertainty, which may be mathematically modeled by intervals of utilities. Intervals of utilities may be mapped to singl... Read More about Comparing Intervals Using Type Reduction.

FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language (2020)
Presentation / Conference Contribution
Chen, C., Razak, T. R., & Garibaldi, J. M. (2020, July). FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language. Presented at 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK

This paper presents an R package FuzzyR which is an extended fuzzy logic toolbox for the R programming language. FuzzyR is a continuation of the previous Fuzzy R toolboxes such as FuzzyToolkitUoN. Whilst keeping existing functionalities of the previo... Read More about FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language.

Just–In–Time Supply Chain Management Using Interval Type–2 Fuzzy Decision Making (2019)
Presentation / Conference Contribution
Runkler, T. A., Chen, C., Coupland, S., & John, R. (2019, June). Just–In–Time Supply Chain Management Using Interval Type–2 Fuzzy Decision Making. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

We propose the application of interval type-2 fuzzy decision making (IT2FDM) to dynamic scheduling of deliveries in a just-in-time logistic process. Delivery decisions are based on order priorities computed from the expected decrease of customer sati... Read More about Just–In–Time Supply Chain Management Using Interval Type–2 Fuzzy Decision Making.

A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm" (2018)
Journal Article
Chen, C., Wu, D., Garibaldi, J. M., John, R., Twycross, J., & Mendel, J. M. (2018). A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm". IEEE Transactions on Fuzzy Systems, 26(6), 3905-3907. https://doi.org/10.1109/tfuzz.2018.2865134

This letter is a supplement to the previous paper “A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm”. In the previous paper, the enhanced iterative algorithm with stop condition (EIASC) was shown to be the most ineff... Read More about A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm".

Type reduction operators for interval type–2 defuzzification (2018)
Journal Article
Runkler, T. A., Chen, C., & John, R. (2018). Type reduction operators for interval type–2 defuzzification. Information Sciences, 467, 464-476. https://doi.org/10.1016/j.ins.2018.08.023

Fuzzy sets are an important approach to model uncertainty. Defuzzification maps fuzzy sets to non–fuzzy (crisp) values. Type–2 fuzzy sets model uncertainty in the degree of membership in a fuzzy set. Type–2 defuzzification maps type–2 fuzzy sets to n... Read More about Type reduction operators for interval type–2 defuzzification.

Interval Type–2 Defuzzification Using Uncertainty Weights (2017)
Book Chapter
Runkler, T. A., Coupland, S., John, R., & Chen, C. (2018). Interval Type–2 Defuzzification Using Uncertainty Weights. In S. Mostaghim, A. Nürnberger, & C. Borgelt (Eds.), Frontiers in Computational Intelligence (47-59). Springer Verlag. https://doi.org/10.1007/978-3-319-67789-7_4

© Springer International Publishing AG 2018. One of the most popular interval type–2 defuzzification methods is the Karnik–Mendel (KM) algorithm. Nie and Tan (NT) have proposed an approximation of the KM method that converts the interval type–2 membe... Read More about Interval Type–2 Defuzzification Using Uncertainty Weights.

Type-1 and interval type-2 ANFIS: a comparison (2017)
Presentation / Conference Contribution
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. Type-1 and interval type-2 ANFIS: a comparison. Presented at 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017)

In a previous paper, we proposed an extended ANFIS architecture and showed that interval type-2 ANFIS produced larger errors than type-1 ANFIS on the well-known IRIS classification problem. In this paper, more experiments on both synthetic and real-w... Read More about Type-1 and interval type-2 ANFIS: a comparison.

A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm (2017)
Journal Article
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. (2018). A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm. IEEE Transactions on Fuzzy Systems, 26(2), 1079-1085. https://doi.org/10.1109/tfuzz.2017.2699168

The Karnik-Mendel algorithm is used to compute the centroid of interval type-2 fuzzy sets, determining the switch points needed for the lower and upper bounds of the centroid, through an iterative process. It is commonly acknowledged that there is n... Read More about A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm.

A new accuracy measure based on bounded relative error for time series forecasting (2017)
Journal Article
Chen, C., Twycross, J., & Garibaldi, J. M. (2017). A new accuracy measure based on bounded relative error for time series forecasting. PLoS ONE, 12(3), Article e0174202. https://doi.org/10.1371/journal.pone.0174202

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising c... Read More about A new accuracy measure based on bounded relative error for time series forecasting.