A new Mercer sigmoid kernel for clinical data classification. - André M. Carrington; Paul W. Fieguth; Helen H. Chen (EMBC, 2014)

Measures of Model Interpretability for Model Selection. - André M. Carrington; Paul W. Fieguth; Helen H. Chen (CD-MAKE, 2018)

Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care. - André M. Carrington (2018)

Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations. - Andreas Holzinger; André M. Carrington; Heimo Müller (CoRR, 2019)

Measuring the Quality of Explanations: The System Causability Scale (SCS). - Andreas Holzinger; André M. Carrington; Heimo Müller (Künstliche Intell., 2020)

A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms. - André M. Carrington; Paul W. Fieguth; Hammad Qazi; Andreas Holzinger; Helen H. Chen; Franz Mayr; Douglas G. Manuel (BMC Medical Informatics Decis. Mak., 2020)

Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation. - André M. Carrington; Douglas G. Manuel; Paul W. Fieguth; Tim Ramsay; Venet Osmani; Bernhard Wernly; Carol Bennett; Steven Hawken; Matthew McInnes; Olivia Magwood; Yusuf Sheikh; Andreas Holzinger (CoRR, 2021)

The ROC Diagonal is Not Layperson's Chance: A New Baseline Shows the Useful Area. - André M. Carrington; Paul W. Fieguth; Franz Mayr; Nick D. James; Andreas Holzinger; John W. Pickering; Richard I. Aviv (CD-MAKE, 2022)

Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation. - André M. Carrington; Douglas G. Manuel; Paul W. Fieguth; Tim Ramsay; Venet Osmani; Bernhard Wernly; Carol Bennett; Steven Hawken; Olivia Magwood; Yusuf Sheikh; Matthew McInnes; Andreas Holzinger (IEEE Trans. Pattern Anal. Mach. Intell., 2023)