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Dr CHAO CHEN's Outputs (13)

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.

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.

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.

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.

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.

Performance and Interpretability in Fuzzy Logic Systems – can we have both? (2020)
Presentation / Conference Contribution
Pekaslan, D., Chen, C., Wagner, C., & Garibaldi, J. M. (2020, June). Performance and Interpretability in Fuzzy Logic Systems – can we have both?. Presented at 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU2020, Lisbon, Portugal (held online)

Fuzzy Logic Systems can provide a good level of interpretability and may provide a key building block as part of a growing interest in explainable AI. In practice, the level of interpretability of a given fuzzy logic system is dependent on how well i... Read More about Performance and Interpretability in Fuzzy Logic Systems – can we have both?.

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.

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.

An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models (2016)
Presentation / Conference Contribution
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models. Presented at 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016)

In this paper, an extended ANFIS architecture is proposed. By incorporating an extra layer for the fuzzification process, the extended architecture is able to fit both type-1 and interval type-2 models. The learning properties of the proposed archite... Read More about An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models.