All Outputs (86)

Explaining time series classifiers through meaningful perturbation and optimisation (2023)
Journal Article
Meng, H., Wagner, C., & Triguero, I. (2023). Explaining time series classifiers through meaningful perturbation and optimisation. Information Sciences, 645, Article 119334. https://doi.org/10.1016/j.ins.2023.119334

Machine learning approaches have enabled increasingly powerful time series classifiers. While performance has improved drastically, the resulting classifiers generally suffer from poor explainability, limiting their applicability in critical areas. S... Read More about Explaining time series classifiers through meaningful perturbation and optimisation.

Assessing responsible innovation training (2023)
Journal Article
Stahl, B. C., Aicardi, C., Brooks, L., Craigon, P. J., Cunden, M., Burton, S. D., …Webb, H. (2023). Assessing responsible innovation training. Journal of Responsible Technology, 16, Article 100063. https://doi.org/10.1016/j.jrt.2023.100063

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be... Read More about Assessing responsible innovation training.

Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression (2023)
Journal Article
Kabir, S., Wagner, C., & Ellerby, Z. (2023). Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression. IEEE Transactions on Artificial Intelligence, 1-19. https://doi.org/10.1109/TAI.2023.3234930

Most of statistics and AI draw insights through modelling discord or variance between sources (i.e., inter-source) of information. Increasingly however, research is focusing on uncertainty arising at the level of individual measurements (i.e., within... Read More about Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression.

Feature Importance Identification for Time Series Classifiers (2022)
Conference Proceeding
Meng, H., Wagner, C., & Triguero, I. (2022). Feature Importance Identification for Time Series Classifiers. In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (3293-3298). https://doi.org/10.1109/smc53654.2022.9945205

Time series classification is a challenging research area where machine learning techniques such as deep learning perform well, yet lack interpretability. Identifying the most important features for such classifiers provides a pathway to improving th... Read More about Feature Importance Identification for Time Series Classifiers.

Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression (2022)
Conference Proceeding
Pekaslan, D., & Wagner, C. (2022). Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression. In IEEE World Congress on Computational Intelligence (IEEE WCCI2022). https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882840

The compositional representation of data and associated statistical approaches is a powerful framework for modelling and reasoning about quantities which reflect proportions of a whole. Recently, an increasing body of work has started exploring the a... Read More about Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression.

Does Permitting Uncertain Estimates Help or Hinder the Wisdom of Crowds? (2022)
Conference Proceeding
Ellerby, Z., & Wagner, C. (2022). Does Permitting Uncertain Estimates Help or Hinder the Wisdom of Crowds?. In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882802

This paper adds to a growing body of research into the practical utility of using interval-valued (IV) response modes to efficiently capture richer quantitative data from people-e.g., through surveys. Specifically, IV responses offer a cohesive metho... Read More about Does Permitting Uncertain Estimates Help or Hinder the Wisdom of Crowds?.

Visualization of Interval Regression for Facilitating Data and Model Insight (2022)
Conference Proceeding
Kabir, S., & Wagner, C. (2022). Visualization of Interval Regression for Facilitating Data and Model Insight. In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882717

With growing significance of interval-valued data, interest in artificial intelligence methods tailored to this data type is similarly increasing across a range of application domains. Here, regression, i.e., the modelling of the association between... Read More about Visualization of Interval Regression for Facilitating Data and Model Insight.

Tackling communication and analytical problems in environmental planning: Expert assessment of key definitions and their relationships (2022)
Journal Article
Wallace, K. J., Wagner, C., Pannell, D. J., Kim, M. K., & Rogers, A. A. (2022). Tackling communication and analytical problems in environmental planning: Expert assessment of key definitions and their relationships. Journal of Environmental Management, 317, Article 115352. https://doi.org/10.1016/j.jenvman.2022.115352

Inadequate definition of key terms and their relationships generates significant communication and analytical problems in environmental planning. In this work, we evaluate an ontological framework for environmental planning designed to combat these p... Read More about Tackling communication and analytical problems in environmental planning: Expert assessment of key definitions and their relationships.

Constraint reformulations for set point optimization problems using fuzzy cognitive map models (2021)
Journal Article
Garzón Casado, A., Cano Marchal, P., Wagner, C., Gómez Ortega, J., & Gámez García, J. (2022). Constraint reformulations for set point optimization problems using fuzzy cognitive map models. Optimal Control Applications and Methods, 43(3), 711-721. https://doi.org/10.1002/oca.2846

The selection of optimal set points is an important problem in modern process control. Fuzzy cognitive maps (FCMs) allow to construct models of complex processes using expert knowledge, which is particularly useful in situations where measuring the v... Read More about Constraint reformulations for set point optimization problems using fuzzy cognitive map models.

Capturing richer information: On establishing the validity of an interval-valued survey response mode (2021)
Journal Article
Ellerby, Z., Wagner, C., & Broomell, S. B. (2022). Capturing richer information: On establishing the validity of an interval-valued survey response mode. Behavior Research Methods, 54, 1240-1262. https://doi.org/10.3758/s13428-021-01635-0

Obtaining quantitative survey responses that are both accurate and informative is crucial to a wide range of fields. Traditional and ubiquitous response formats such as Likert and visual analogue scales require condensation of responses into discrete... Read More about Capturing richer information: On establishing the validity of an interval-valued survey response mode.

Do People Prefer to Give Interval-Valued or Point Estimates and Why? (2021)
Conference Proceeding
Ellerby, Z., & Wagner, C. (2021). Do People Prefer to Give Interval-Valued or Point Estimates and Why?. In Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021) (1-6). https://doi.org/10.1109/FUZZ45933.2021.9494507

Capturing interval-valued, as opposed to more conventional point-valued data, offers a potentially efficient method of obtaining richer information in individual responses. In turn, interval-valued data provide a strong foundation for subsequent fuzz... Read More about Do People Prefer to Give Interval-Valued or Point Estimates and Why?.

Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox (2021)
Conference Proceeding
Razak, T. R., Chen, C., Garibaldi, J. M., & Wagner, C. (2021). Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ45933.2021.9494485

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)
Conference Proceeding
Chen, C., Zhao, Y., Wagner, C., Pekaslan, D., & Garibaldi, J. M. (2021). An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzz45933.2021.9494472

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.

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems (2020)
Journal Article
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2021). A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 29(11), 3323-3333. https://doi.org/10.1109/TFUZZ.2020.3018379

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainabil... Read More about A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems.

Choosing Sample Sizes for Statistical Measures on Interval-Valued Data (2020)
Conference Proceeding
McCulloch, J., Ellerby, Z., & Wagner, C. (2020). Choosing Sample Sizes for Statistical Measures on Interval-Valued Data. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-8). https://doi.org/10.1109/FUZZ48607.2020.9177745

Intervals have frequently been used in the literature to represent uncertainty in data, from eliciting uncertain judgements from experts to representing uncertainty in sensor measurements. This widespread use of intervals has led to research on inter... Read More about Choosing Sample Sizes for Statistical Measures on Interval-Valued Data.

Insights from interval-valued ratings of consumer products - a DECSYS appraisal (2020)
Conference Proceeding
Ellerby, Z., Miles, O., McCulloch, J., & Wagner, C. (2020). Insights from interval-valued ratings of consumer products - a DECSYS appraisal. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-8). https://doi.org/10.1109/FUZZ48607.2020.9177634

The capture and analysis of interval-valued data has seen increased interest over recent years. This offers a direct means to capture and reason about uncertainty in data, whether obtained from sensors or from people. Open-source software (DECSYS [1]... Read More about Insights from interval-valued ratings of consumer products - a DECSYS appraisal.

Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java (2020)
Conference Proceeding
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2020). Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java. In Proceedings of IEEE World Congress on Computational Intelligence (WCCI) 2020

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.

Performance and Interpretability in Fuzzy Logic Systems – can we have both? (2020)
Conference Proceeding
Pekaslan, D., Chen, C., Wagner, C., & Garibaldi, J. M. (2020). Performance and Interpretability in Fuzzy Logic Systems – can we have both?.

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?.

A Similarity Measure Based on Bidirectional Subsethood for Intervals (2020)
Journal Article
Kabir, S., Wagner, C., Havens, T. C., & Anderson, D. T. (2020). A Similarity Measure Based on Bidirectional Subsethood for Intervals. IEEE Transactions on Fuzzy Systems, 28(11), 2890-2904. https://doi.org/10.1109/tfuzz.2019.2945249

With a growing number of areas leveraging interval-valued data—including in the context of modelling human uncertainty (e.g., in Cyber Security), the capacity to accurately and systematically compare intervals for reasoning and computation is increas... Read More about A Similarity Measure Based on Bidirectional Subsethood for Intervals.

Uncertainty-Aware Forecasting of Renewable Energy Sources (2020)
Conference Proceeding
Pekaslan, D., Wagner, C., Garibaldi, J. M., Marín, L. G., & Sáez, D. (2020). Uncertainty-Aware Forecasting of Renewable Energy Sources. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). https://doi.org/10.1109/bigcomp48618.2020.00-68

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.