Outputs (4)

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis (2020)
Journal Article
Chernbumroong, S., Johnson, J., Gupta, N., Miller, S., Mccormack, F. X., Garibaldi, J. M., & Johnson, S. R. (2021). Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis. European Respiratory Journal, 57(6), Article 2003036. https://doi.org/10.1183/13993003.03036-2020

Background: Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to ident... Read More about Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis.

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 Comprehensive Study of the Efficiency of Type-Reduction Algorithms (2020)
Journal Article
Chen, C., Wu, D., Garibaldi, J. M., John, R. I., Twycross, J., & Mendel, J. M. (2021). A Comprehensive Study of the Efficiency of Type-Reduction Algorithms. IEEE Transactions on Fuzzy Systems, 29(6), 1556 -1566. https://doi.org/10.1109/tfuzz.2020.2981002

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there have been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karni... Read More about A Comprehensive Study of the Efficiency of Type-Reduction Algorithms.

Constrained Interval Type-2 Fuzzy Sets (2020)
Journal Article
Dalterio, P., Garibaldi, J. M., John, R., & Pourabdollah, A. (2021). Constrained Interval Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 29(5), 1212-1225. https://doi.org/10.1109/tfuzz.2020.2970911

In many contexts, type-2 fuzzy sets are obtained from a type-1 fuzzy set to which we wish to add uncertainty. However, in the current type-2 representation there is no restriction on the shape of the footprint of uncertainty and the embedded sets tha... Read More about Constrained Interval Type-2 Fuzzy Sets.