Professor CHRISTIAN WAGNER's Outputs (84)

On the Potential of Fuzzy Integral-based Decision-level Fusion when the Fuzzy Measure is Informed by Densities Alone (2025)
Presentation / Conference Contribution
Huang, Y., & Wagner, C. (2025, July). On the Potential of Fuzzy Integral-based Decision-level Fusion when the Fuzzy Measure is Informed by Densities Alone. Presented at 2025 IEEE International Conference on Fuzzy Systems, Reims, France

Aggregation is key technique in decision-level fusion of classifiers. Beyond affording performance in an ensemble-sense, such aggregation operator based fusion also has the potential to add a substantial layer of interpretability to the overall syste... Read More about On the Potential of Fuzzy Integral-based Decision-level Fusion when the Fuzzy Measure is Informed by Densities Alone.

A python library for data-driven causal fuzzy classification rule generation --mablars (2025)
Presentation / Conference Contribution
Zhang, T., & Wagner, C. (2025, July). A python library for data-driven causal fuzzy classification rule generation --mablars. Presented at 2025 IEEE International Conference on Fuzzy Systems, Reims, France

A principal focus in fuzzy systems research is on maintaining good performance while providing strong explain-ability, principally by leveraging meaningful sets of human-accessible rules. In practice, while a variety of software tools have been devel... Read More about A python library for data-driven causal fuzzy classification rule generation --mablars.

Counterfactual linguistic rule-based explanations based on locally relevant causal mechanisms (2025)
Presentation / Conference Contribution
Zhang, T., & Wagner, C. (2025, July). Counterfactual linguistic rule-based explanations based on locally relevant causal mechanisms. Presented at 2025 IEEE International Conference on Fuzzy Systems, Reims, France

Counterfactual (CF) explanations provide a potentially powerful mechanism to deliver meaningful explanations of AI decisions. CF explanations are convincing when they reflect causal relationships between variables, because humans are cause-effect thi... Read More about Counterfactual linguistic rule-based explanations based on locally relevant causal mechanisms.

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.

Implementing responsible innovation: the role of the meso-level(s) between project and organisation (2024)
Journal Article
Stahl, B. C., Portillo, V., Wagner, H., Craigon, P. J., Darzentas, D., De Ossorno Garcia, S., Dowthwaite, L., Greenhalgh, C., Middleton, S. E., Nichele, E., Wagner, C., & Webb, H. (2024). Implementing responsible innovation: the role of the meso-level(s) between project and organisation. Journal of Responsible Innovation, 11(1), Article 2370934. https://doi.org/10.1080/23299460.2024.2370934

Much of academic discussion of responsible innovation (RI) has focused on RI integration into research projects. In addition, significant attention has also been paid to RI structures and policies at the research policy and institutional level. This... Read More about Implementing responsible innovation: the role of the meso-level(s) between project and organisation.

Interval Agreement Weighted Average - Sensitivity to Data Set Features (2024)
Presentation / Conference Contribution
Zhao, Y., Wagner, C., Ryan, B., Pekaslan, D., & Navarro, J. (2024, June). Interval Agreement Weighted Average - Sensitivity to Data Set Features. Presented at 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Yokohama, Japan

The growing use of intervals in fields like survey analysis necessitates effective aggregation methods that can summarize and represent such uncertain data representations. The Interval Agreement Approach (IAA) addresses this by aggregating interval... Read More about Interval Agreement Weighted Average - Sensitivity to Data Set Features.

Generating Locally Relevant Explanations Using Causal Rule Discovery (2024)
Presentation / Conference Contribution
Zhang, T., & Wagner, C. (2024, June). Generating Locally Relevant Explanations Using Causal Rule Discovery. Presented at 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Yokohama, Japan

In the real-world an effect often arises via multiple causal mechanisms. Conversely, the behaviour of AI systems is commonly driven by correlations which may-or may not-be themselves linked to causal mechanisms in the associated real-world system the... Read More about Generating Locally Relevant Explanations Using Causal Rule Discovery.

SEGAL time series classification — Stable explanations using a generative model and an adaptive weighting method for LIME (2024)
Journal Article
Meng, H., Wagner, C., & Triguero, I. (2024). SEGAL time series classification — Stable explanations using a generative model and an adaptive weighting method for LIME. Neural Networks, 176, Article 106345. https://doi.org/10.1016/j.neunet.2024.106345

Local Interpretability Model-agnostic Explanations (LIME) is a well-known post-hoc technique for explaining black-box models. While very useful, recent research highlights challenges around the explanations generated. In particular, there is a potent... Read More about SEGAL time series classification — Stable explanations using a generative model and an adaptive weighting method for LIME.

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.

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.

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., Heaver, M. D., Saille, S. D., Dolby, S., Dowthwaite, L., Eke, D., Hughes, S., Keene, P., Kuh, V., Portillo, V., Shanley, D., Smallman, M., Smith, M., Stilgoe, J., Ulnicane, I., …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, 5(1), 3-22. 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)
Presentation / Conference Contribution
Meng, H., Wagner, C., & Triguero, I. (2022, October). Feature Importance Identification for Time Series Classifiers. Presented at 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic

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)
Presentation / Conference Contribution
Pekaslan, D., & Wagner, C. (2022, July). Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression. Presented at 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022), Padova, Italy

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)
Presentation / Conference Contribution
Ellerby, Z., & Wagner, C. (2022, July). Does Permitting Uncertain Estimates Help or Hinder the Wisdom of Crowds?. Presented at 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Padua, Italy

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)
Presentation / Conference Contribution
Kabir, S., & Wagner, C. (2022, July). Visualization of Interval Regression for Facilitating Data and Model Insight. Presented at IEEE World Congress on Computational Intelligence 2022 (IEEE WCCI 2022), Padova, Italy

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.

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.

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.