Evolutionary Machine Learning for Interpretable and eXplainable AI-Reference-Cited by-同舟云学术

Evolutionary Machine Learning for Interpretable and eXplainable AI

Published:2024-07-14 Issue: Volume:2023 Page:1038-1068
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Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Author:

Siddique Abubakar¹^ORCID,Browne Will N.²^ORCID,Urbanowicz Ryan J.³^ORCID

Affiliation:

1. Wellington Institute of Technology, Whitireia WelTec, Wellington, New Zealand

2. Queensland University of Technology, Brisbane, Queensland, Australia

3. Cedars Sinai Medical Center, Los Angeles, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3638530.3658378

Reference38 articles.

1. Escalante, Hugo Jair. "Automated machine learning---a brief review at the end of the early years." Automated Design of Machine Learning and Search Algorithms (2021): 11-28.

2. Holmes, John H., and Jennifer A. Sager. "The EpiXCS workbench: a tool for experimentation and visualization." In International Workshop on Learning Classifier Systems, pp. 333--344. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

3. Hwang Sy Ryan Urbanowicz Selah Lynch Tawnya Vernon Kellie Bresz Carolina Giraldo Erin Kennedy et al. "Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors." JCO clinical cancer informatics 7 (2023): e2200097.

4. Kennedy Erin E. Anahita Davoudi Sy Hwang Philip J. Freda Ryan Urbanowicz Kathryn H. Bowles and Danielle L. Mowery. "Identifying barriers to post-acute care referral and characterizing negative patient preferences among hospitalized older adults using natural language processing." In AMIA Annual Symposium Proceedings vol. 2022 p. 606. American Medical Informatics Association 2022.

5. A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure