Ryan J. Urbanowicz

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 53

Top Topics

Classifier Systems

Feature Selection

Evolutionary Algorithm

Top Venues

GECCO (Companion)

GECCO Companion

Publications

Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
New Pathways in Coevolutionary Computation. CoRR (2024)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
Coevolving Artistic Images Using OMNIREP. CoRR (2024)
Abubakar Siddique, Will N. Browne, Ryan J. Urbanowicz
Modern Applications of Evolutionary Rule-based Machine Learning. GECCO Companion (2023)
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)
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 (2023)
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)
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 (2023)
Erin E. Kennedy, Anahita Davoudi, Sy Hwang, Ryan J. Urbanowicz, Philip Freda, Kathy 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)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE). CoRR (2022)
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)
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 (2022)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems. CoRR (2022)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions. CoRR (2022)
Satvik Dasariraju, Ryan J. Urbanowicz
RARE: evolutionary feature engineering for rare-variant bin discovery. GECCO Companion (2021)
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 (2021)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
Coevolving Artistic Images Using OMNIREP. EvoMUSART (2020)
Siddharth Verma, Piyush Borole, Ryan J. Urbanowicz
Evolving genetic programming trees in a rule-based learning framework. GECCO Companion (2020)
Robert F. Zhang, Ryan J. Urbanowicz
A scikit-learn compatible learning classifier system. GECCO Companion (2020)
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 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)
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 (2020)
Ryan J. Urbanowicz, Moshe Sipper
Evolutionary algorithms in biomedical data mining: challenges, solutions, and frontiers. GECCO Companion (2020)
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) (2020)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
New Pathways in Coevolutionary Computation. GPTP (2019)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions. EuroGP (2019)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems. CEC (2019)
Trang T. Le, Ryan J. Urbanowicz, Jason H. Moore, Brett A. McKinney
STatistical Inference Relief (STIR) feature selection. Bioinform. 35 (8) (2019)
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)
Ryan J. Urbanowicz, Christopher Lo, John H. Holmes, Jason H. Moore
Attribute tracking: strategies towards improved detection and characterization of complex associations. GECCO (2018)
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) (2018)
Moshe Sipper, Ryan J. Urbanowicz, Jason H. Moore
To know the objective is not (necessarily) to know the objective function. BioData Min. 11 (1) (2018)
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 (2018)
Ryan J. Urbanowicz, Danilo Vasconcellos Vargas
Introducing learning classifier systems: rules that capture complexity. GECCO (Companion) (2018)
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 (2018)
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)
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)
Ryan J. Urbanowicz
Introducing rule-based machine learning: capturing complexity. GECCO (Companion) (2017)
Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore
Benchmarking Relief-Based Feature Selection Methods. CoRR (2017)
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 (2017)
Ryan J. Urbanowicz, Melissa Meeker, William G. La Cava, Randal S. Olson, Jason H. Moore
Relief-Based Feature Selection: Introduction and Review. CoRR (2017)
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) (2017)
Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore
Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning. PPSN (2016)
Ryan J. Urbanowicz, Will N. Browne, Karthik Kuber
Hands-on Workshop on Learning Classifier Systems. GECCO (Companion) (2016)
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 (2016)
Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, Jason H. Moore
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. CoRR (2016)
Ryan J. Urbanowicz
Introducing Rule-Based Machine Learning: Capturing Complexity. GECCO (Companion) (2016)
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)
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)
Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore
Pareto Inspired Multi-objective Rule Fitness for Adaptive Rule-based Machine Learning. GECCO (Companion) (2016)
Ryan J. Urbanowicz, Jason H. Moore
ExSTraCS 2.0: description and evaluation of a scalable learning classifier system. Evol. Intell. 8 (2-3) (2015)
Tim Kovacs, Muhammad Iqbal, Kamran Shafi, Ryan J. Urbanowicz
Special issue on the 20th anniversary of XCS. Evol. Intell. 8 (2-3) (2015)
Ryan J. Urbanowicz, Niranjan Ramanand, Jason H. Moore
Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System. GECCO (Companion) (2015)
Ryan J. Urbanowicz, Jason H. Moore
Retooling Fitness for Noisy Problems in a Supervised Michigan-style Learning Classifier System. GECCO (2015)
Ryan J. Urbanowicz, Will N. Browne
Introducing Rule-based Machine Learning: A Practical Guide. GECCO (Companion) (2015)
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)
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 (2014)
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) (2013)
James Rudd, Jason H. Moore, Ryan J. Urbanowicz
A simple multi-core parallelization strategy for learning classifier system evaluation. GECCO (Companion) (2013)
Jie Tan, Jason H. Moore, Ryan J. Urbanowicz
Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System. ECAL (2013)
Will N. Browne, Ryan J. Urbanowicz
Learning classifier systems: introducing the user-friendly textbook. GECCO (Companion) (2013)
Kamran Shafi, Ryan J. Urbanowicz, Muhammad Iqbal
Special issue on advances in Learning Classifier Systems. Evol. Intell. 6 (2) (2013)
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) (2013)
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 (2012)
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)
Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jason H. Moore
Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems. GECCO (2012)
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 (2012)
Daniele Loiacono, Albert Orriols-Puig, Ryan J. Urbanowicz
Special issue on advances in learning classifier systems. Evol. Intell. 5 (2) (2012)
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) (2012)
Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, Jason H. Moore
Random artificial incorporation of noise in a learning classifier system environment. GECCO (Companion) (2011)
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)
Ryan J. Urbanowicz, Jason H. Moore
The application of michigan-style learning classifiersystems to address genetic heterogeneity and epistasisin association studies. GECCO (2010)
Ryan J. Urbanowicz, Nate Barney, Bill C. White, Jason H. Moore
Mask functions for the symbolic modeling of epistasis using genetic programming. GECCO (2008)