[go: up one dir, main page]

skip to main content
10.1145/3638530.3658378acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
tutorial

Evolutionary Machine Learning for Interpretable and eXplainable AI

Published: 01 August 2024 Publication History
First page of PDF

References

[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]
Kohn, Rachel, Michael O. Harhay, Gary E. Weissman, Ryan Urbanowicz, Wei Wang, George L. Anesi, Stefania Scott et al. "A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure." Journal of Medical Systems 47, no. 1 (2023): 83.
[6]
Murata, Michael M., Fumie Igari, Ryan Urbanowicz, Lila Mouakkad, Sungjin Kim, Zijing Chen, Dolores DiVizio, Edwin M. Posadas, Armando E. Giuliano, and Hisashi Tanaka. "A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA." bioRxiv (2023): 2023-10.
[7]
Olson, Randal S., and Jason H. Moore. "TPOT: A tree-based pipeline optimization tool for automating machine learning." Workshop on automatic machine learning. PMLR, 2016.
[8]
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should i trust you?" Explaining the predictions of any classifier." In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135--1144. 2016.
[9]
Rudin, Cynthia. "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead." Nature machine intelligence 1, no. 5 (2019): 206-215.
[10]
Tong, Boning, Shannon L. Risacher, Jingxuan Bao, Yanbo Feng, Xinkai Wang, Marylyn D. Ritchie, Jason H. Moore, Ryan Urbanowicz, Andrew J. Saykin, and Li Shen. "Comparing amyloid imaging normalization strategies for Alzheimer's disease classification using an automated machine learning pipeline." AMIA Summits on Translational Science Proceedings 2023 (2023): 525.
[11]
Urbanowicz, Ryan J., Ambrose Granizo-Mackenzie, and Jason H. Moore. "An analysis pipeline with statistical and visualization-guided knowledge discovery for michigan-style learning classifier systems." IEEE computational intelligence magazine 7, no. 4 (2012): 35-45.
[12]
Urbanowicz, Ryan J., Christopher Lo, John H. Holmes, and Jason H. Moore. "Attribute tracking: strategies towards improved detection and characterization of complex associations." In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 553--560. 2018.
[13]
Urbanowicz, Ryan J., Harsh Bandhey, Brendan T. Keenan, Greg Maislin, Sy Hwang, Danielle L. Mowery, Shannon M. Lynch et al. "STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers." arXiv preprint arXiv:2312.05461 (2023).
[14]
Urbanowicz, Ryan J., and Jason H. Moore. "ExSTraCS 2.0: description and evaluation of a scalable learning classifier system." Evolutionary intelligence 8 (2015): 89-116.
[15]
Urbanowicz, Ryan, Robert Zhang, Yuhan Cui, and Pranshu Suri. "STREAMLINE: a simple, transparent, endto-end automated machine learning pipeline facilitating data analysis and algorithm comparison." In Genetic Programming Theory and Practice XIX, pp. 201--231. Singapore: Springer Nature Singapore, 2023.
[16]
Urbanowicz, Ryan J., and Will N. Browne. Introduction to learning classifier systems. Springer, 2017.
[17]
Wang, Xinkai, Yanbo Feng, Boning Tong, Jingxuan Bao, Marylyn D. Ritchie, Andrew J. Saykin, Jason H. Moore, Ryan Urbanowicz, and Li Shen. "Exploring automated machine learning for cognitive outcome prediction from multimodal brain imaging using streamline." AMIA Summits on Translational Science Proceedings 2023 (2023): 544.
[18]
Zhang, Robert, Rachael Stolzenberg-Solomon, Shannon M. Lynch, and Ryan J. Urbanowicz. "LCS-DIVE: An automated rule-based machine learning visualization pipeline for characterizing complex associations in classification." arXiv preprint arXiv:2104.12844 (2021).
[19]
Zhang, Robert F., and Ryan J. Urbanowicz. "A scikit-learn compatible learning classifier system." In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1816--1823. 2020.
[20]
J. Holland and J. Reitman, "Cognitive systems based on adaptive algorithms reprinted in: Evolutionary computation. the fossil record," IEEE Press, New York, 1998.
[21]
J. H. Holland et al., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.
[22]
J. Holland and D. Goldberg, "Genetic algorithms in search, optimization and machine learning," Massachusetts: Addison-Wesley, 1989.
[23]
S. W. Wilson, "ZCS: A zeroth level classifier system," Evolutionary computation, vol. 2, no. 1, pp. 1--18, 1994.
[24]
S. W. Wilson, "Classifier fitness based on accuracy," Evolutionary Computation, vol. 3, no. 2, pp. 149--175, 1995.
[25]
M. V. Butz, T. Kovacs, P. L. Lanzi, and S.W.Wilson, "Toward a theory of generalization and learning in XCS," IEEE Transactions on Evolutionary Computation, vol. 8, no. 1, pp. 28--46, 2004.
[26]
A. Siddique, W. N. Browne, and G. M. Grimshaw, "Lateralized learning for robustness against adversarial attacks in a visual classification system," in Proc. Genet. Evol. Comput. Conf., 2020, pp. 395--403.
[27]
A. Siddique, W. N. Browne, and G. M. Grimshaw, "Frames-of-reference based learning: Overcoming perceptual aliasing in multistep decision making tasks," IEEE Trans. Evol. Comput., vol. 26, no. 1, pp. 174--187, Feb. 2022
[28]
A. Siddique, W. N. Browne, and G. M. Grimshaw, "Learning classifier systems: Appreciating the lateralized approach," in Proc. Genet. Evol. Comput. Conf. Comput., 2020, pp. 1807--1815.
[29]
H. A. Shehu, A. Siddique, W. N. Browne, and H. Eisenbarth, "Lateralized approach for robustness against attacks in emotion categorization from images," in Proc. Int. Conf. Appl. Evol. Comput. (Part EvoStar), 2021, pp. 469--485
[30]
A. Siddique, "Lateralized learning to solve complex problems," Ph.D. dissertation, Open Access Te Herenga Waka-Victoria University of Wellington, 2021
[31]
Siddique, A., Iqbal, M., Din, S., & Browne, W. (2023, July). Attention in Rule-Based Machine Learning: Exploiting Learning Classifier Systems' Generalization for Image Classification. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp. 323--326).
[32]
Browne, W., Holford, K., Moore, C., & Bullock, J. (1998). A practical application of a learning classifier system in a steel hot strip mill. In Artificial Neural Nets and Genetic Algorithms (pp. 611--614). Springer, Vienna.
[33]
Siddique, A., Browne, W. N. & Grimshaw, G. M. (2023). Lateralized Learning to Solve Complex Boolean Problems. IEEE Transactions on Cybernetics, 53(11), 6761--6775.
[34]
Andersen, H., Lensen, A., Browne, W. N. & Mei, Y. (2023). Producing Diverse Rashomon Sets of Counterfactual Explanations with Niching Particle Swarm Optimization Algorithms. GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference, 393--401. Association for Computing Machinery (ACM).
[35]
Andersen, H., Lensen, A. & Browne, W. N. (2022). Improving the search of learning classifier systems through interpretable feature clustering. GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1752--1756. Association for Computing Machinery (ACM).
[36]
Bishop, J. T., Gallagher, M. & Browne, W. N. (2022). Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS. GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, 323--331. Association for Computing Machinery (ACM).
[37]
Nguyen, T. B., Browne, W. N. & Zhang, M. (2023). ConCS: A Continual Classifier System for Continual Learning of Multiple Boolean Problems. IEEE Transactions on Evolutionary Computation, 27(4), 1057--1071.
[38]
Liang, Megan, Palado, Gabrielle, & Browne, Will N. (2019) Identifying Simple Shapes to Classify the Big Picture. In Proceedings of the 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ 2019). Institute of Electrical and Electronics Engineers Inc., United States of America, pp. 1--6.

Index Terms

  1. Evolutionary Machine Learning for Interpretable and eXplainable AI
            Index terms have been assigned to the content through auto-classification.

            Recommendations

            Comments

            Information & Contributors

            Information

            Published In

            cover image ACM Conferences
            GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
            July 2024
            2187 pages
            ISBN:9798400704956
            DOI:10.1145/3638530
            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

            Sponsors

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            Published: 01 August 2024

            Check for updates

            Qualifiers

            • Tutorial

            Conference

            GECCO '24 Companion
            Sponsor:

            Acceptance Rates

            Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

            Contributors

            Other Metrics

            Bibliometrics & Citations

            Bibliometrics

            Article Metrics

            • 0
              Total Citations
            • 34
              Total Downloads
            • Downloads (Last 12 months)34
            • Downloads (Last 6 weeks)13
            Reflects downloads up to 16 Oct 2024

            Other Metrics

            Citations

            View Options

            Get Access

            Login options

            View options

            PDF

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader

            Media

            Figures

            Other

            Tables

            Share

            Share

            Share this Publication link

            Share on social media