All Outputs (71)

A comparison between two types of Fuzzy TOPSIS method (2015)
Conference Proceeding
Madi, E., Garibaldi, J. M., & Wagner, C. (2015). A comparison between two types of Fuzzy TOPSIS method.

Multi Criteria Decision Making methods have been developed to solve complex real-world decision problems. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is currently one of the most popular methods and has been shown to p... Read More about A comparison between two types of Fuzzy TOPSIS method.

Data-informed fuzzy measures for fuzzy integration of intervals and fuzzy numbers (2014)
Journal Article
Havens, T. C., Anderson, D. T., & Wagner, C. (2015). Data-informed fuzzy measures for fuzzy integration of intervals and fuzzy numbers. IEEE Transactions on Fuzzy Systems, 23(5), https://doi.org/10.1109/TFUZZ.2014.2382133

The fuzzy integral (FI) with respect to a fuzzy measure (FM) is a powerful means of aggregating information. The most popular FIs are the Choquet and Sugeno, and most research focuses on these two variants. The arena of the FM is much more populated,... Read More about Data-informed fuzzy measures for fuzzy integration of intervals and fuzzy numbers.

Analysing fuzzy sets through combining measures of similarity and distance (2014)
Conference Proceeding
McCulloch, J., Wagner, C., & Aickelin, U. (2014). Analysing fuzzy sets through combining measures of similarity and distance.

Reasoning with fuzzy sets can be achieved through measures such as similarity and distance. However, these measures can often give misleading results when considered independently, for example giving the same value for two different pairs of fuzzy se... Read More about Analysing fuzzy sets through combining measures of similarity and distance.

Comparison of distance metrics for hierarchical data in medical databases (2014)
Conference Proceeding
Hassan, D., Aickelin, U., & Wagner, C. (2014). Comparison of distance metrics for hierarchical data in medical databases.

Distance metrics are broadly used in different research areas and applications, such as bio-informatics, data mining and many other fields. However, there are some metrics, like pg-gram and Edit Distance used specifically for data with a hierarchical... Read More about Comparison of distance metrics for hierarchical data in medical databases.

A fuzzy directional distance measure (2014)
Conference Proceeding
McCulloch, J., Hinde, C., Wagner, C., & Aickelin, U. (2014). A fuzzy directional distance measure.

The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory, however, distance measures currently within the literature use a crisp value to represent the distance between fuzzy sets. A real valued distance measure is... Read More about A fuzzy directional distance measure.

From Interval-Valued Data to General Type-2 Fuzzy Sets (2014)
Journal Article
Wagner, C., Miller, S., Garibaldi, J. M., Anderson, D. T., & Havens, T. C. (2015). From Interval-Valued Data to General Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 23(2), 248-269. https://doi.org/10.1109/tfuzz.2014.2310734

In this paper, a new approach is presented to model interval-based data using fuzzy sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or mu... Read More about From Interval-Valued Data to General Type-2 Fuzzy Sets.

Extension of the Fuzzy Integral for General Fuzzy Set-Valued Information (2014)
Journal Article
Anderson, D. T., Havens, T. C., Wagner, C., Keller, J. M., Anderson, M. F., & Wescott, D. J. (2014). Extension of the Fuzzy Integral for General Fuzzy Set-Valued Information. IEEE Transactions on Fuzzy Systems, 22(6), 1625-1639. https://doi.org/10.1109/TFUZZ.2014.2302479

The fuzzy integral (FI) is an extremely flexible aggregation operator. It is used in numerous applications, such as image processing, multicriteria decision making, skeletal age-at-death estimation, and multisource (e.g., feature, algorithm, sensor,... Read More about Extension of the Fuzzy Integral for General Fuzzy Set-Valued Information.

Measuring the directional distance between fuzzy sets (2013)
Conference Proceeding
McCulloch, J., Wagner, C., & Aickelin, U. (2013). Measuring the directional distance between fuzzy sets.

The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory. However, current distance measures within the literature do not account for the direction of change between fuzzy sets; a useful concept in a variety of app... Read More about Measuring the directional distance between fuzzy sets.

Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system
Conference Proceeding
Hibshi, H., Breaux, T. D., & Wagner, C. (in press). Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system.

Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expe... Read More about Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system.

Extending similarity measures of interval type-2 fuzzy sets to general type-2 fuzzy sets
Conference Proceeding
McCulloch, J., Wagner, C., & Aickelin, U. Extending similarity measures of interval type-2 fuzzy sets to general type-2 fuzzy sets.

Similarity measures provide one of the core tools that enable reasoning about fuzzy sets. While many types of similarity measures exist for type-1 and interval type-2 fuzzy sets, there are very few similarity measures that enable the comparison of... Read More about Extending similarity measures of interval type-2 fuzzy sets to general type-2 fuzzy sets.

Creating personalised energy plans: from groups to individuals using Fuzzy C Means Clustering
Conference Proceeding
Dent, I., Wagner, C., Aickelin, U., & Rodden, T. Creating personalised energy plans: from groups to individuals using Fuzzy C Means Clustering.

Changes in the UK electricity market mean that domestic users will be required to modify their usage behaviour in accordance with energy eciency targets. Clustering allows usage data, collected at the household level, to be clustered into groups... Read More about Creating personalised energy plans: from groups to individuals using Fuzzy C Means Clustering.