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ORCIDRyan J. Urbanowicz
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2020 – today
- 2024
[j22]Emily Wong, Ryan J. Urbanowicz
, Tiffani J. Bright, Nicholas P. Tatonetti, Yi-Wen Hsiao, Xiuzhen Huang, Jason H. Moore, Pei-Chen Peng:
Advancing LGBTQ+ inclusion in STEM education and AI research. Patterns 5(6): 101010 (2024)- [c42]Harsh Bandhey, Sphia Sadek, Malek Kamoun, Ryan J. Urbanowicz:
Evolutionary Feature-Binning with Adaptive Burden Thresholding for Biomedical Risk Stratification. EvoApplications@EvoStar 2024: 225-239 - [c41]Abubakar Siddique, Will N. Browne, Ryan J. Urbanowicz:
Evolutionary Machine Learning for Interpretable and eXplainable AI. GECCO Companion 2024: 1038-1068 - [c40]Alexa A. Woodward, Harsh Bandhey, Jason H. Moore, Ryan J. Urbanowicz:
Survival-LCS: A Rule-Based Machine Learning Approach to Survival Analysis. GECCO 2024 - [i14]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
New Pathways in Coevolutionary Computation. CoRR abs/2401.10515 (2024) - [i13]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
Coevolving Artistic Images Using OMNIREP. CoRR abs/2401.11167 (2024) - 2023
- [j21]Jesse G. Meyer, Ryan J. Urbanowicz, Patrick C. N. Martin, Karen O'Connor, Ruowang Li, Pei-Chen Peng, Tiffani J. Bright, Nicholas P. Tatonetti, Kyoung-Jae Won, Graciela Gonzalez-Hernandez, Jason H. Moore:
ChatGPT and large language models in academia: opportunities and challenges. BioData Min. 16(1) (2023) - [j20]Satvik Dasariraju, Loren Gragert, Grace L. Wager, Keith McCullough, Nicholas K. Brown, Malek Kamoun, Ryan J. Urbanowicz:
HLA amino acid Mismatch-Based risk stratification of kidney allograft failure using a novel Machine learning algorithm. J. Biomed. Informatics 142: 104374 (2023) - [j19]Rachel Kohn, Michael O. Harhay, Gary E. Weissman, Ryan J. Urbanowicz, Wei Wang, George L. Anesi, Stefania Scott, Brian Bayes, S. Ryan Greysen, Scott D. Halpern, Meeta Prasad Kerlin:
A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure. J. Medical Syst. 47(1): 83 (2023) - [c39]Abubakar Siddique, Will N. Browne, Ryan J. Urbanowicz:
Modern Applications of Evolutionary Rule-based Machine Learning. GECCO Companion 2023: 1301-1330 - [c38]Ryan J. Urbanowicz, Harsh Bandhey, Malek Kamoun, Nolan Fogarty, Yi-An Hsieh:
Scikit-FIBERS: An 'OR'-Rule Discovery Evolutionary Algorithm for Risk Stratification in Right-Censored Survival Analyses. GECCO Companion 2023: 1846-1854 - [c37]Audrey Yang, Sam Kamien, Anahita Davoudi, Sy Hwang, Meet Gandhi, Ryan J. Urbanowicz, Danielle L. Mowery:
Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing. MedInfo 2023: 619-623 - [i12]Ryan J. Urbanowicz, Harsh Bandhey, Brendan T. Keenan, Greg Maislin, Sy Hwang, Danielle L. Mowery, Shannon M. Lynch, Diego R. Mazzotti, Fang Han, Qing Yun Li, Thomas Penzel, Sergio Tufik, Lia Bittencourt, Thorarinn Gislason, Philip de Chazal, Bhajan Singh, Nigel McArdle, Ning-Hung Chen, Allan Pack, Richard J. Schwab, Peter A. Cistulli, Ulysses J. Magalang:
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers. CoRR abs/2312.05461 (2023) - 2022
- [j18]Alexa A. Woodward, Deanne M. Taylor, Elizabeth Goldmuntz, Laura E. Mitchell, A. J. Agopian, Jason H. Moore, Ryan J. Urbanowicz:
Gene-Interaction-Sensitive enrichment analysis in congenital heart disease. BioData Min. 15(1) (2022) - [c36]Erin E. Kennedy, Anahita Davoudi, Sy Hwang, Ryan J. Urbanowicz, Philip Freda, Kathryn H. Bowles, Danielle L. Mowery:
Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing. AMIA 2022 - [c35]Ryan J. Urbanowicz, Robert Zhang, Yuhan Cui, Pranshu Suri:
STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison. GPTP 2022: 201-231 - [i11]Ryan J. Urbanowicz, Robert Zhang, Yuhan Cui, Pranshu Suri:
STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison. CoRR abs/2206.12002 (2022) - [i10]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions. CoRR abs/2206.12707 (2022) - [i9]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems. CoRR abs/2206.13509 (2022) - [i8]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE). CoRR abs/2206.15409 (2022) - 2021
- [c34]Satvik Dasariraju, Ryan J. Urbanowicz:
RARE: evolutionary feature engineering for rare-variant bin discovery. GECCO Companion 2021: 1335-1343 - [i7]Robert Zhang, Rachael Stolzenberg-Solomon, Shannon M. Lynch, Ryan J. Urbanowicz:
LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification. CoRR abs/2104.12844 (2021) - 2020
- [j17]Jason H. Moore, Ian Barnett, Mary Regina Boland, Yong Chen, George Demiris, Graciela Gonzalez-Hernandez, Daniel S. Herman, Blanca E. Himes, Rebecca A. Hubbard, Dokyoon Kim, Jeffrey S. Morris, Danielle L. Mowery, Marylyn D. Ritchie, Li Shen, Ryan J. Urbanowicz, John H. Holmes:
Ideas for how informaticians can get involved with COVID-19 research. BioData Min. 13(1): 3 (2020) - [c33]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
Coevolving Artistic Images Using OMNIREP. EvoMUSART 2020: 165-178 - [c32]Siddharth Verma, Piyush Borole, Ryan J. Urbanowicz:
Evolving genetic programming trees in a rule-based learning framework. GECCO Companion 2020: 233-234 - [c31]Ryan J. Urbanowicz, Moshe Sipper:
Evolutionary algorithms in biomedical data mining: challenges, solutions, and frontiers. GECCO Companion 2020: 1224-1253 - [c30]Robert F. Zhang, Ryan J. Urbanowicz:
A scikit-learn compatible learning classifier system. GECCO Companion 2020: 1816-1823 - [c29]Camillia Matuk, Susan A. Yoon, Joseph L. Polman, Anna Amato, Jacob Barton, Nicole Bulalacao, Francesco Cafaro, Lina Chopra Haldar, Amanda M. Cottone, Krista Cortes, Kayla DesPortes, Tim Erickson, William Finzer, Katie Headrick Taylor, Beth Herbel-Eisenmann, Cynthia Graville, Kris D. Gutiérrez, Traci Higgins, Blanca E. Himes, Kathryn A. Lanouette, Hollylynne Lee, Vivian Lim, M. Lisette Lopez, Leilah Lyons, Dan Milz, Maria C. Olivares, Elizabeth Osche, Tapan S. Parikh, Thomas M. Philip, Laurie Rubel, Joey Shelley, Edward Rivero, Jessica Roberts, Collette Roberto, Tony Petrosino, Andee Rubin, Jooeun Shim, Megan Silander, Stephen Sommer, David Stokes, Marian Tes, Milka Trajkova, Ryan J. Urbanowicz, Ralph Vacca, Sarah Van Wart, Veena Vasudevan, Michelle Hoda Wilkerson, Peter J. Woods:
Data Literacy for Social Justice. ICLS 2020 - [i6]Ryan J. Urbanowicz, Pranshu Suri, Yuhan Cui, Jason H. Moore, Karen Ruth, Rachael Stolzenberg-Solomon, Shannon M. Lynch:
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments. CoRR abs/2008.12829 (2020)
2010 – 2019
- 2019
- [j16]Trang T. Le, Ryan J. Urbanowicz, Jason H. Moore, Brett A. McKinney:
STatistical Inference Relief (STIR) feature selection. Bioinform. 35(8): 1358-1365 (2019) - [c28]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems. CEC 2019: 1868-1874 - [c27]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions. EuroGP 2019: 146-161 - [c26]Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz:
New Pathways in Coevolutionary Computation. GPTP 2019: 295-305 - [c25]Yancy Lo, Selah F. Lynch, Ryan J. Urbanowicz, Randal S. Olson, Ashley Z. Ritter, Christina R. Whitehouse, Melissa O'Connor, Susan K. Keim, Margaret V. McDonald, Jason H. Moore, Kathryn H. Bowles:
Using Machine Learning on Home Health Care Assessments to Predict Fall Risk. MedInfo 2019: 684-688 - 2018
- [j15]Shefali S. Verma, Anastasia Lucas, Xinyuan Zhang, Yogasudha Veturi, Scott M. Dudek, Binglan Li, Ruowang Li, Ryan J. Urbanowicz, Jason H. Moore, Dokyoon Kim, Marylyn D. Ritchie:
Collective feature selection to identify crucial epistatic variants. BioData Min. 11(1): 5:1-5:22 (2018) - [j14]Moshe Sipper, Ryan J. Urbanowicz, Jason H. Moore:
To know the objective is not (necessarily) to know the objective function. BioData Min. 11(1): 21:1-21:3 (2018) - [j13]Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore:
Benchmarking relief-based feature selection methods for bioinformatics data mining. J. Biomed. Informatics 85: 168-188 (2018) - [j12]Ryan J. Urbanowicz, Melissa Meeker, William G. La Cava, Randal S. Olson, Jason H. Moore:
Relief-based feature selection: Introduction and review. J. Biomed. Informatics 85: 189-203 (2018) - [c24]Ryan J. Urbanowicz, Christopher Lo, John H. Holmes, Jason H. Moore:
Attribute tracking: strategies towards improved detection and characterization of complex associations. GECCO 2018: 553-560 - [c23]Ryan J. Urbanowicz, Danilo Vasconcellos Vargas:
Introducing learning classifier systems: rules that capture complexity. GECCO (Companion) 2018: 619-648 - 2017
- [b1]Ryan J. Urbanowicz, Will N. Browne:
Introduction to Learning Classifier Systems. Springer Briefs in Intelligent Systems, Springer 2017, ISBN 978-3-662-55006-9, pp. 1-123 - [j11]Randal S. Olson, William G. La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore:
PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min. 10(1): 36:1-36:13 (2017) - [c22]Ryan J. Urbanowicz:
Introducing rule-based machine learning: capturing complexity. GECCO (Companion) 2017: 576-604 - [c21]Ryan J. Urbanowicz, Ben Yang, Jason H. Moore:
Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System. GPTP 2017: 55-71 - [c20]Randal S. Olson, Moshe Sipper, William G. La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore:
A System for Accessible Artificial Intelligence. GPTP 2017: 121-134 - [i5]Randal S. Olson, William G. La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore:
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison. CoRR abs/1703.00512 (2017) - [i4]Ryan J. Urbanowicz, Melissa Meeker, William G. La Cava, Randal S. Olson, Jason H. Moore:
Relief-Based Feature Selection: Introduction and Review. CoRR abs/1711.08421 (2017) - [i3]Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore:
Benchmarking Relief-Based Feature Selection Methods. CoRR abs/1711.08477 (2017) - 2016
- [c19]Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore:
Automating Biomedical Data Science Through Tree-Based Pipeline Optimization. EvoApplications (1) 2016: 123-137 - [c18]Ryan J. Urbanowicz:
Introducing Rule-Based Machine Learning: Capturing Complexity. GECCO (Companion) 2016: 305-332 - [c17]Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, Jason H. Moore:
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. GECCO 2016: 485-492 - [c16]Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore:
Pareto Inspired Multi-objective Rule Fitness for Adaptive Rule-based Machine Learning. GECCO (Companion) 2016: 1403 - [c15]Ryan J. Urbanowicz, Will N. Browne, Karthik Kuber:
Hands-on Workshop on Learning Classifier Systems. GECCO (Companion) 2016: 1407-1408 - [c14]Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore:
Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning. PPSN 2016: 514-524 - [i2]Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore:
Automating biomedical data science through tree-based pipeline optimization. CoRR abs/1601.07925 (2016) - [i1]Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, Jason H. Moore:
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. CoRR abs/1603.06212 (2016) - 2015
- [j10]Tim Kovacs, Muhammad Iqbal, Kamran Shafi, Ryan J. Urbanowicz:
Special issue on the 20th anniversary of XCS. Evol. Intell. 8(2-3): 51-53 (2015) - [j9]Ryan J. Urbanowicz, Jason H. Moore:
ExSTraCS 2.0: description and evaluation of a scalable learning classifier system. Evol. Intell. 8(2-3): 89-116 (2015) - [c13]Ryan J. Urbanowicz, Will N. Browne:
Introducing Rule-based Machine Learning: A Practical Guide. GECCO (Companion) 2015: 263-292 - [c12]Ryan J. Urbanowicz, Jason H. Moore:
Retooling Fitness for Noisy Problems in a Supervised Michigan-style Learning Classifier System. GECCO 2015: 591-598 - [c11]Ryan J. Urbanowicz, Niranjan Ramanand, Jason H. Moore:
Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System. GECCO (Companion) 2015: 1029-1036 - 2014
- [j8]Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jeff Kiralis, Jason H. Moore:
A Classification and Characterization of Two-Locus, Pure, Strict, Epistatic Models for Simulation and Detection. BioData Min. 7: 8 (2014) - [c10]Ryan J. Urbanowicz, Gediminas Bertasius, Jason H. Moore:
An Extended Michigan-Style Learning Classifier System for Flexible Supervised Learning, Classification, and Data Mining. PPSN 2014: 211-221 - 2013
- [j7]Kamran Shafi, Ryan J. Urbanowicz, Muhammad Iqbal:
Special issue on advances in Learning Classifier Systems. Evol. Intell. 6(2): 55-56 (2013) - [j6]James Rudd, Jason H. Moore, Ryan J. Urbanowicz:
A multi-core parallelization strategy for statistical significance testing in learning classifier systems. Evol. Intell. 6(2): 127-134 (2013) - [j5]Ryan J. Urbanowicz, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore:
Research and applications: Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach. J. Am. Medical Informatics Assoc. 20(4): 603-612 (2013) - [c9]Jie Tan, Jason H. Moore, Ryan J. Urbanowicz:
Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System. ECAL 2013: 110-117 - [c8]Will N. Browne, Ryan J. Urbanowicz:
Learning classifier systems: introducing the user-friendly textbook. GECCO (Companion) 2013: 439-468 - [c7]James Rudd, Jason H. Moore, Ryan J. Urbanowicz:
A simple multi-core parallelization strategy for learning classifier system evaluation. GECCO (Companion) 2013: 1259-1266 - 2012
- [j4]Ryan J. Urbanowicz, Jeff Kiralis, Jonathan M. Fisher, Jason H. Moore:
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection. BioData Min. 5: 15 (2012) - [j3]Ryan J. Urbanowicz, Jeff Kiralis, Nicholas A. Sinnott-Armstrong, Tamra Heberling, Jonathan M. Fisher, Jason H. Moore:
GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Min. 5: 16 (2012) - [j2]Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jason H. Moore:
An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems. IEEE Comput. Intell. Mag. 7(4): 35-45 (2012) - [j1]Daniele Loiacono, Albert Orriols-Puig, Ryan J. Urbanowicz:
Special issue on advances in learning classifier systems. Evol. Intell. 5(2): 57-58 (2012) - [c6]Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jason H. Moore:
Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems. GECCO 2012: 927-934 - [c5]Ryan J. Urbanowicz, Delaney Granizo-MacKenzie, Jason H. Moore:
Using Expert Knowledge to Guide Covering and Mutation in a Michigan Style Learning Classifier System to Detect Epistasis and Heterogeneity. PPSN (1) 2012: 266-275 - 2011
- [c4]Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, Jason H. Moore:
Random artificial incorporation of noise in a learning classifier system environment. GECCO (Companion) 2011: 369-374 - 2010
- [c3]Ryan J. Urbanowicz, Jason H. Moore:
The application of michigan-style learning classifiersystems to address genetic heterogeneity and epistasisin association studies. GECCO 2010: 195-202 - [c2]Ryan J. Urbanowicz, Jason H. Moore:
The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies. PPSN (1) 2010: 404-413
2000 – 2009
- 2008
- [c1]Ryan J. Urbanowicz, Nate Barney, Bill C. White, Jason H. Moore:
Mask functions for the symbolic modeling of epistasis using genetic programming. GECCO 2008: 339-346
Coauthor Index
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