All Outputs (8)

Exploring How Component Factors and Their Uncertainty Affect Judgements of Risk in Cyber-Security (2019)
Conference Proceeding
Ellerby, Z., McCulloch, J., Wilson, M., & Wagner, C. (2020). Exploring How Component Factors and Their Uncertainty Affect Judgements of Risk in Cyber-Security. In Critical Information Infrastructures Security: 14th International Conference, CRITIS 2019, Linköping, Sweden, September 23–25, 2019, Revised Selected Papers (31-42). https://doi.org/10.1007/978-3-030-37670-3_3

Subjective judgements from experts provide essential information when assessing and modelling threats in respect to cyber-physical systems. For example, the vulnerability of individual system components can be described using multiple factors, such a... Read More about Exploring How Component Factors and Their Uncertainty Affect Judgements of Risk in Cyber-Security.

A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering (2019)
Conference Proceeding
Razak, T. R., Garibaldi, J. M., & Wagner, C. (2019). A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) ( 1-7). https://doi.org/10.1109/FUZZ-IEEE.2019.8859011

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.

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels (2019)
Conference Proceeding
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2019). Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-7). https://doi.org/10.1109/FUZZ-IEEE.2019.8858800

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.

Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures (2019)
Conference Proceeding
Agrawal, U., Wagner, C., Garibaldi, J. M., & Soria, D. (2019). Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures. In 2019 IEEE International Conference on Fuzzy Systems (1-7). https://doi.org/10.1109/FUZZ-IEEE.2019.8858821

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.

DECSYS - Discrete and Ellipse-based response Capture SYStem (2019)
Conference Proceeding
Ellerby, Z., McCulloch, J., Young, J., & Wagner, C. (2019). DECSYS - Discrete and Ellipse-based response Capture SYStem. . https://doi.org/10.1109/FUZZ-IEEE.2019.8858996

Data-driven techniques that capture uncertainty through intervals or fuzzy sets can substantially improve systematic reasoning about uncertain information. Recent years have seen renewed interest in the capture of intervals from a variety of sources-... Read More about DECSYS - Discrete and Ellipse-based response Capture SYStem.

On Comparing and Selecting Approaches to Model Interval-Valued Data as Fuzzy Sets (2019)
Conference Proceeding
McCulloch, J., Ellerby, Z., & Wagner, C. (2019). On Comparing and Selecting Approaches to Model Interval-Valued Data as Fuzzy Sets. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/FUZZ-IEEE.2019.8858993

The capture of interval-valued data is becoming an increasingly common approach in data collection (from survey based research to the collation of sensor data) as an efficient method of obtaining information about uncertainty associated with the data... Read More about On Comparing and Selecting Approaches to Model Interval-Valued Data as Fuzzy Sets.

Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy Sets (2019)
Conference Proceeding
Navarro, J., & Wagner, C. (2019). Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy Sets

Recently, there has been much research into modelling of uncertainty in human perception through Fuzzy Sets (FSs). Most of this research has focused on allowing respondents to express their (intra) uncertainty using intervals. Here, depending on the... Read More about Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy Sets.

Measuring similarity between discontinuous intervals : challenges and solutions (2019)
Conference Proceeding
Kabir, S., Wagner, C., Havens, T. C., & Anderson, D. T. (2019). Measuring similarity between discontinuous intervals : challenges and solutions. In Proceedings of 2019 IEEE Conference on Fuzzy Systems

Discontinuous intervals (DIs) arise in a wide range of contexts, from real world data capture of human opinion to ?-cuts of non-convex fuzzy sets. Commonly, for assessing the similarity of DIs, the latter are converted into their continuous form, fol... Read More about Measuring similarity between discontinuous intervals : challenges and solutions.