Ryan J. Urbanowicz

According to our database, Ryan J. Urbanowicz authored at least 69 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Advancing LGBTQ+ inclusion in STEM education and AI research.

Emily Wong, Ryan J. Urbanowicz, Tiffani J. Bright, Nicholas P. Tatonetti, Yi-Wen Hsiao, Xiuzhen Huang, Jason H. Moore, Pei-Chen Peng

Patterns, 2024

Survival-LCS: A Rule-Based Machine Learning Approach to Survival Analysis.

Alexa A. Woodward, Harsh Bandhey, Jason H. Moore, Ryan J. Urbanowicz

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

Evolutionary Machine Learning for Interpretable and eXplainable AI.

Abubakar Siddique, Will N. Browne, Ryan J. Urbanowicz

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

Evolutionary Feature-Binning with Adaptive Burden Thresholding for Biomedical Risk Stratification.

Harsh Bandhey, Sphia Sadek, Malek Kamoun, Ryan J. Urbanowicz

Proceedings of the Applications of Evolutionary Computation - 27th European Conference, 2024

2023

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

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

J. Medical Syst., December, 2023

HLA amino acid Mismatch-Based risk stratification of kidney allograft failure using a novel Machine learning algorithm.

Satvik Dasariraju, Loren Gragert, Grace L. Wager, Keith McCullough, Nicholas K. Brown, Malek Kamoun, Ryan J. Urbanowicz

J. Biomed. Informatics, June, 2023

ChatGPT and large language models in academia: opportunities and challenges.

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

BioData Min., January, 2023

STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers.

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

CoRR, 2023

Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing.

Audrey Yang, Sam Kamien, Anahita Davoudi, Sy Hwang, Meet Gandhi, Ryan J. Urbanowicz, Danielle L. Mowery

Proceedings of the MEDINFO 2023 - The Future Is Accessible, 2023

Scikit-FIBERS: An 'OR'-Rule Discovery Evolutionary Algorithm for Risk Stratification in Right-Censored Survival Analyses.

Ryan J. Urbanowicz, Harsh Bandhey, Malek Kamoun, Nolan Fogarty, Yi-An Hsieh

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Modern Applications of Evolutionary Rule-based Machine Learning.

Abubakar Siddique, Will N. Browne, Ryan J. Urbanowicz

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

2022

Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE).

Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

CoRR, 2022

Gene-Interaction-Sensitive enrichment analysis in congenital heart disease.

Alexa A. Woodward, Deanne M. Taylor, Elizabeth Goldmuntz, Laura E. Mitchell, A. J. Agopian, Jason H. Moore, Ryan J. Urbanowicz

BioData Min., 2022

STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison.

Ryan J. Urbanowicz, Robert Zhang, Yuhan Cui, Pranshu Suri

Proceedings of the Genetic Programming Theory and Practice XIX [GPTP 2022], 2022

Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.

Erin E. Kennedy, Anahita Davoudi, Sy Hwang, Ryan J. Urbanowicz, Philip Freda, Kathryn H. Bowles, Danielle L. Mowery

Proceedings of the AMIA 2022, 2022

2021

LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification.

Robert Zhang, Rachael Stolzenberg-Solomon, Shannon M. Lynch, Ryan J. Urbanowicz

CoRR, 2021

RARE: evolutionary feature engineering for rare-variant bin discovery.

Satvik Dasariraju, Ryan J. Urbanowicz

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

2020

A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments.

Ryan J. Urbanowicz, Pranshu Suri, Yuhan Cui, Jason H. Moore, Karen Ruth, Rachael Stolzenberg-Solomon, Shannon M. Lynch

CoRR, 2020

Ideas for how informaticians can get involved with COVID-19 research.

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

BioData Min., 2020

Data Literacy for Social Justice.

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

Proceedings of the Interdisciplinarity in the Learning Sciences: Proceedings of the 14th International Conference of the Learning Sciences, 2020

A scikit-learn compatible learning classifier system.

Robert F. Zhang, Ryan J. Urbanowicz

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Evolving genetic programming trees in a rule-based learning framework.

Siddharth Verma, Piyush Borole, Ryan J. Urbanowicz

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Evolutionary algorithms in biomedical data mining: challenges, solutions, and frontiers.

Ryan J. Urbanowicz, Moshe Sipper

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Coevolving Artistic Images Using OMNIREP.

Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

Proceedings of the Artificial Intelligence in Music, Sound, Art and Design, 2020

2019

STatistical Inference Relief (STIR) feature selection.

Trang T. Le, Ryan J. Urbanowicz, Jason H. Moore, Brett A. McKinney

Bioinform., 2019

Using Machine Learning on Home Health Care Assessments to Predict Fall Risk.

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

Proceedings of the MEDINFO 2019: Health and Wellbeing e-Networks for All, 2019

New Pathways in Coevolutionary Computation.

Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

Proceedings of the Genetic Programming Theory and Practice XVII [GPTP 2019, 2019

Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions.

Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

Proceedings of the Genetic Programming - 22nd European Conference, 2019

Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems.

Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

Proceedings of the IEEE Congress on Evolutionary Computation, 2019

2018

Benchmarking relief-based feature selection methods for bioinformatics data mining.

Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore

J. Biomed. Informatics, 2018

Relief-based feature selection: Introduction and review.

Ryan J. Urbanowicz, Melissa Meeker, William G. La Cava, Randal S. Olson, Jason H. Moore

J. Biomed. Informatics, 2018

Collective feature selection to identify crucial epistatic variants.

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

BioData Min., 2018

To know the objective is not (necessarily) to know the objective function.

Moshe Sipper, Ryan J. Urbanowicz, Jason H. Moore

BioData Min., 2018

Introducing learning classifier systems: rules that capture complexity.

Ryan J. Urbanowicz, Danilo Vasconcellos Vargas

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Attribute tracking: strategies towards improved detection and characterization of complex associations.

Ryan J. Urbanowicz, Christopher Lo, John H. Holmes, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

2017

Introduction to Learning Classifier Systems

Ryan J. Urbanowicz, Will N. Browne

Springer Briefs in Intelligent Systems, Springer, ISBN: 978-3-662-55006-9, 2017

Benchmarking Relief-Based Feature Selection Methods.

Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore

CoRR, 2017

PMLB: a large benchmark suite for machine learning evaluation and comparison.

Randal S. Olson, William G. La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore

BioData Min., 2017

Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System.

Ryan J. Urbanowicz, Ben Yang, Jason H. Moore

Proceedings of the Genetic Programming Theory and Practice XV, 2017

A System for Accessible Artificial Intelligence.

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

Proceedings of the Genetic Programming Theory and Practice XV, 2017

2016

Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning.

Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

Pareto Inspired Multi-objective Rule Fitness for Adaptive Rule-based Machine Learning.

Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2016

Hands-on Workshop on Learning Classifier Systems.

Ryan J. Urbanowicz, Will N. Browne, Karthik Kuber

Proceedings of the Genetic and Evolutionary Computation Conference, 2016

Introducing Rule-Based Machine Learning: Capturing Complexity.

Ryan J. Urbanowicz

Proceedings of the Genetic and Evolutionary Computation Conference, 2016

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science.

Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, Jason H. Moore

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization.

Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore

Proceedings of the Applications of Evolutionary Computation - 19th European Conference, 2016

2015

ExSTraCS 2.0: description and evaluation of a scalable learning classifier system.

Ryan J. Urbanowicz, Jason H. Moore

Evol. Intell., 2015

Special issue on the 20th anniversary of XCS.

Tim Kovacs, Muhammad Iqbal, Kamran Shafi, Ryan J. Urbanowicz

Evol. Intell., 2015

Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System.

Ryan J. Urbanowicz, Niranjan Ramanand, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

Retooling Fitness for Noisy Problems in a Supervised Michigan-style Learning Classifier System.

Ryan J. Urbanowicz, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

Introducing Rule-based Machine Learning: A Practical Guide.

Ryan J. Urbanowicz, Will N. Browne

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

2014

A Classification and Characterization of Two-Locus, Pure, Strict, Epistatic Models for Simulation and Detection.

Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jeff Kiralis, Jason H. Moore

BioData Min., 2014

An Extended Michigan-Style Learning Classifier System for Flexible Supervised Learning, Classification, and Data Mining.

Ryan J. Urbanowicz, Gediminas Bertasius, Jason H. Moore

Proceedings of the Parallel Problem Solving from Nature - PPSN XIII, 2014

2013

Research and applications: Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach.

Ryan J. Urbanowicz, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore

J. Am. Medical Informatics Assoc., 2013

Special issue on advances in Learning Classifier Systems.

Kamran Shafi, Ryan J. Urbanowicz, Muhammad Iqbal

Evol. Intell., 2013

A multi-core parallelization strategy for statistical significance testing in learning classifier systems.

James Rudd, Jason H. Moore, Ryan J. Urbanowicz

Evol. Intell., 2013

A simple multi-core parallelization strategy for learning classifier system evaluation.

James Rudd, Jason H. Moore, Ryan J. Urbanowicz

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Learning classifier systems: introducing the user-friendly textbook.

Will N. Browne, Ryan J. Urbanowicz

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System.

Jie Tan, Jason H. Moore, Ryan J. Urbanowicz

Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, 2013

2012

Special issue on advances in learning classifier systems.

Daniele Loiacono, Albert Orriols-Puig, Ryan J. Urbanowicz

Evol. Intell., 2012

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems.

Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jason H. Moore

IEEE Comput. Intell. Mag., 2012

GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.

Ryan J. Urbanowicz, Jeff Kiralis, Nicholas A. Sinnott-Armstrong, Tamra Heberling, Jonathan M. Fisher, Jason H. Moore

BioData Min., 2012

Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection.

Ryan J. Urbanowicz, Jeff Kiralis, Jonathan M. Fisher, Jason H. Moore

BioData Min., 2012

Using Expert Knowledge to Guide Covering and Mutation in a Michigan Style Learning Classifier System to Detect Epistasis and Heterogeneity.

Ryan J. Urbanowicz, Delaney Granizo-MacKenzie, Jason H. Moore

Proceedings of the Parallel Problem Solving from Nature - PPSN XII, 2012

Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems.

Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

2011

Random artificial incorporation of noise in a learning classifier system environment.

Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, Jason H. Moore

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

2010

The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies.

Ryan J. Urbanowicz, Jason H. Moore

Proceedings of the Parallel Problem Solving from Nature, 2010

The application of michigan-style learning classifiersystems to address genetic heterogeneity and epistasisin association studies.

Ryan J. Urbanowicz, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2010

2008

Mask functions for the symbolic modeling of epistasis using genetic programming.

Ryan J. Urbanowicz, Nate Barney, Bill C. White, Jason H. Moore

Proceedings of the Genetic and Evolutionary Computation Conference, 2008