Relief-based feature selection: Introduction and review RJ Urbanowicz, M Meeker, W La Cava, RS Olson, JH Moore Journal of biomedical informatics 85, 189-203, 2018 | 1158 | 2018 |
Evaluation of a tree-based pipeline optimization tool for automating data science RS Olson, N Bartley, RJ Urbanowicz, JH Moore Proceedings of the genetic and evolutionary computation conference 2016, 485-492, 2016 | 684 | 2016 |
PMLB: a large benchmark suite for machine learning evaluation and comparison RS Olson, W La Cava, P Orzechowski, RJ Urbanowicz, JH Moore BioData mining 10, 1-13, 2017 | 397 | 2017 |
Learning classifier systems: a complete introduction, review, and roadmap RJ Urbanowicz, JH Moore Journal of Artificial Evolution and Applications 2009 (1), 736398, 2009 | 389 | 2009 |
Automating biomedical data science through tree-based pipeline optimization RS Olson, RJ Urbanowicz, PC Andrews, NA Lavender, LC Kidd, ... Applications of Evolutionary Computation: 19th European Conference …, 2016 | 334 | 2016 |
Benchmarking relief-based feature selection methods for bioinformatics data mining RJ Urbanowicz, RS Olson, P Schmitt, M Meeker, JH Moore Journal of biomedical informatics 85, 168-188, 2018 | 254 | 2018 |
GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures RJ Urbanowicz, J Kiralis, NA Sinnott-Armstrong, T Heberling, JM Fisher, ... BioData mining 5, 1-14, 2012 | 234 | 2012 |
ChatGPT and large language models in academia: opportunities and challenges JG Meyer, RJ Urbanowicz, PCN Martin, K O’Connor, R Li, PC Peng, ... BioData Mining 16 (1), 20, 2023 | 148 | 2023 |
ExSTraCS 2.0: description and evaluation of a scalable learning classifier system RJ Urbanowicz, JH Moore Evolutionary intelligence 8, 89-116, 2015 | 128 | 2015 |
Introduction to learning classifier systems RJ Urbanowicz, WN Browne Springer, 2017 | 119 | 2017 |
Analysis of gene‐gene interactions D Gilbert‐Diamond, JH Moore Current protocols in human genetics 70 (1), 1.14. 1-1.14. 12, 2011 | 82 | 2011 |
Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach RJ Urbanowicz, AS Andrew, MR Karagas, JH Moore Journal of the American Medical Informatics Association 20 (4), 603-612, 2013 | 77 | 2013 |
An analysis pipeline with statistical and visualization-guided knowledge discovery for michigan-style learning classifier systems RJ Urbanowicz, A Granizo-Mackenzie, JH Moore IEEE computational intelligence magazine 7 (4), 35-45, 2012 | 65 | 2012 |
Statistical inference relief (STIR) feature selection TT Le, RJ Urbanowicz, JH Moore, BA McKinney Bioinformatics 35 (8), 1358-1365, 2019 | 62 | 2019 |
Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems R Urbanowicz, A Granizo-Mackenzie, J Moore Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012 | 47 | 2012 |
The application of michigan-style learning classifiersystems to address genetic heterogeneity and epistasisin association studies RJ Urbanowicz, JH Moore Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010 | 45 | 2010 |
Rapid rule compaction strategies for global knowledge discovery in a supervised learning classifier system J Tan, J Moore, R Urbanowicz Artificial Life Conference Proceedings, 110-117, 2013 | 39 | 2013 |
Preparing next-generation scientists for biomedical big data: artificial intelligence approaches JH Moore, MR Boland, PG Camara, H Chervitz, G Gonzalez, BE Himes, ... Personalized medicine 16 (3), 247-257, 2019 | 35 | 2019 |
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection RJ Urbanowicz, J Kiralis, JM Fisher, JH Moore BioData mining 5, 1-13, 2012 | 35 | 2012 |
A system for accessible artificial intelligence RS Olson, M Sipper, WL Cava, S Tartarone, S Vitale, W Fu, ... Genetic programming theory and practice XV, 121-134, 2018 | 34 | 2018 |