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All Outputs (26)

Optimising rule-based classification in temporal data (2016)
Journal Article
Fattah, P., Aickelin, U., & Wagner, C. (2016). Optimising rule-based classification in temporal data. Zanco Journal of Pure and Applied Sciences, 28(2),

This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in... Read More about Optimising rule-based classification in temporal data.

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems (2016)
Journal Article
Pourabdollah, A., Wagner, C., Aladi, J. H., & Garibaldi, J. M. (2016). Improved Uncertainty Capture for Nonsingleton Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 24(6), 1513-1524. https://doi.org/10.1109/TFUZZ.2016.2540065

© 2016 IEEE. In nonsingleton fuzzy logic systems (NSFLSs), input uncertainties are modeled with input fuzzy sets in order to capture input uncertainty (e.g., sensor noise). The performance of NSFLSs in handling such uncertainties depends on both the... Read More about Improved Uncertainty Capture for Nonsingleton Fuzzy Systems.

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.

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.