All Outputs (121)

Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems
Presentation / Conference Contribution
Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018, October). Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems. Presented at 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan

Real-world environments face a wide range of noise (uncertainty) sources and gaining insight into the level of noise is a critical part of many applications. While Non-Singleton Fuzzy Logic Systems (NSFLSs), in particular recently introduced advanced... Read More about Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems.

Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework
Presentation / Conference Contribution
Figueredo, G. P., Wagner, C., Garibaldi, J. M., & Aickelin, U. (2015, August). Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework. Presented at Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, Helsinki, Finland

In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provid... Read More about Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework.

Tuning a multiple classifier system for side effect discovery using genetic algorithms
Presentation / Conference Contribution
Reps, J. M., Aickelin, U., & Garibaldi, J. M. (2014, July). Tuning a multiple classifier system for side effect discovery using genetic algorithms. Presented at Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China

In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used wi... Read More about Tuning a multiple classifier system for side effect discovery using genetic algorithms.

Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems — A User Study
Presentation / Conference Contribution
Rosli Razak, T. R., Garibaldi, J. M., Wagner, C., Pourabdollah, A., & Soria, D. (2018, November). Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems — A User Study. Presented at 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India

In recent years, researchers have become increasingly more interested in designing an interpretable Fuzzy Logic System (FLS). Many studies have claimed that reducing the complexity of FLSs can lead to improved model interpretability. That is, reducin... Read More about Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems — A User Study.

A classification-regression deep learning model for people counting
Presentation / Conference Contribution
Xu, B., Zou, W., Garibaldi, J., & Qiu, G. (2018, September). A classification-regression deep learning model for people counting. Presented at Intelligent Systems Conference 2018 (IntelliSys 2018), London, UK

In this paper, we construct a multi-task deep learning model to simultaneously predict people number and the level of crowd density. Motivated by the success of applying " ambiguous labelling " to age estimation problem, we also manage to employ this... Read More about A classification-regression deep learning model for people counting.

A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening
Presentation / Conference Contribution
Figueredo, G. P., Shi, P., Parkes, A. J., Evans, K., Garibaldi, J. M., Negm, O., Tighe, P. J., Sewell, H. F., & Robertson, J. (2019, June). A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

Current methods to identify cutoff values for tumour-associated molecules (antigens) discrimination are based on statistics and brute force. These methods applied to cancer screening problems are very inefficient, especially with large data sets with... Read More about A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening.

A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering
Presentation / Conference Contribution
Razak, T. R., Garibaldi, J. M., & Wagner, C. (2019, June). A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering. Presented at International Conference on Fuzzy Systems (FUZZ-IEEE 2019), New Orleans, USA

Hierarchical fuzzy systems (HFSs) have been seen as an effective approach to reduce the complexity of fuzzy logic systems (FLSs), largely as a result of reducing the number of rules. However, it is not clear completely how complexity of HFSs can be m... Read More about A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering.

Direct Application of Convolutional Neural Network Features to Image Quality Assessment
Presentation / Conference Contribution
Hou, X., Sun, K., Liu, B., Gong, Y., Garibaldi, J., & Qiu, G. (2018, December). Direct Application of Convolutional Neural Network Features to Image Quality Assessment. Presented at IEEE Visual Communications and Image Processing (VCIP 2018), Taichung, Taiwan

© 2018 IEEE. We take advantage of the popularity of deep con-volutional neural networks (CNNs) and have developed a very simple image quality assessment method that rivals state of the art. We show that convolutional layer outputs (deep features) of... Read More about Direct Application of Convolutional Neural Network Features to Image Quality Assessment.

Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures
Presentation / Conference Contribution
Agrawal, U., Wagner, C., Garibaldi, J. M., & Soria, D. (2019, June). Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Aggregation operators are mathematical functions that enable the fusion of information from multiple sources. Fuzzy Integrals (FIs) are widely used aggregation operators, which combine information in respect to a Fuzzy Measure (FM) which captures the... Read More about Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures.

A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets
Presentation / Conference Contribution
Shen, Z., Chen, X., & Garibaldi, J. M. (2019, June). A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of fuzzy set... Read More about A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets.

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels
Presentation / Conference Contribution
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2019, June). Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input... Read More about Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels.

Uncertainty-Aware Forecasting of Renewable Energy Sources
Presentation / Conference Contribution
Pekaslan, D., Wagner, C., Garibaldi, J. M., Marín, L. G., & Sáez, D. (2020, February). Uncertainty-Aware Forecasting of Renewable Energy Sources. Presented at 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea (South)

Smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable energy sources, where it is vital t... Read More about Uncertainty-Aware Forecasting of Renewable Energy Sources.

Performance and Interpretability in Fuzzy Logic Systems – can we have both?
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?.

Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI)
Presentation / Conference Contribution
D'Alterio, P., Garibaldi, J. M., & John, R. I. (2020, July). Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI). Presented at IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK

In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the eme... Read More about Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI).

Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java
Presentation / Conference Contribution
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2020, July). Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java. Presented at IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK

Constrained interval type-2 (CIT2) fuzzy sets are a class of type-2 fuzzy sets that has been recently proposed as a way to extend type-1 membership functions to interval type-2 (IT2) while keeping a semantic connection between the IT2 fuzzy set and t... Read More about Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java.

FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language
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.

Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox
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.

FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation
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
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.

Counterfactual rule generation for fuzzy rule-based classification systems
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.