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Contemporary Symbolic Regression Methods and their Relative Performance


Jul 29, 2021
William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore

* To appear in Neurips 2021 Track on Datasets and Benchmarks. Main text: 10 pages, 3 figures; Appendix: 7 pages, 8 figures. https://openreview.net/forum?id=xVQMrDLyGst 

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Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims


Jul 22, 2021
Roland Albert A. Romero, Mariefel Nicole Y. Deypalan, Suchit Mehrotra, John Titus Jungao, Natalie E. Sheils, Elisabetta Manduchi, Jason H. Moore

* 22 pages, 8 figures, 7 tables 

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Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers


Jul 14, 2021
Patryk Orzechowski, Jason H. Moore

* 12 pages, 3 figures with subfigures 

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EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia


May 03, 2021
Paweł Renc, Patryk Orzechowski, Aleksander Byrski, Jarosław Wąs, Jason H. Moore

* 9 pages, 11 figures 

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PMLB v1.0: an open source dataset collection for benchmarking machine learning methods


Nov 30, 2020
Trang T. Le, William La Cava, Joseph D. Romano, John T. Gregg, Daniel J. Goldberg, Praneel Chakraborty, Natasha L. Ray, Daniel Himmelstein, Weixuan Fu, Jason H. Moore

* 6 pages, 2 figures 

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


Sep 08, 2020
Ryan J. Urbanowicz, Pranshu Suri, Yuhan Cui, Jason H. Moore, Karen Ruth, Rachael Stolzenberg-Solomon, Shannon M. Lynch

* 22 pages, 12 figures 

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Benchmarking in Optimization: Best Practice and Open Issues


Jul 07, 2020
Thomas Bartz-Beielstein, Carola Doerr, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise


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Is deep learning necessary for simple classification tasks?


Jun 11, 2020
Joseph D. Romano, Trang T. Le, Weixuan Fu, Jason H. Moore

* 14 pages, 5 figures, 3 tables 

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Genetic programming approaches to learning fair classifiers


Apr 28, 2020
William La Cava, Jason H. Moore

* 9 pages, 7 figures. GECCO 2020 

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SGP-DT: Semantic Genetic Programming Based on Dynamic Targets


Jan 30, 2020
Stefano Ruberto, Valerio Terragni, Jason H. Moore

* 16 pages, European Conference on Genetic Programming (EuroGP 20) 

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Evaluating recommender systems for AI-driven data science


Jun 07, 2019
William La Cava, Heather Williams, Weixuan Fu, Jason H. Moore

* 14 pages, 6 figures 

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Semantic variation operators for multidimensional genetic programming


Apr 18, 2019
William La Cava, Jason H. Moore

* 9 pages, 8 figures, GECCO 2019 

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Interpretation of machine learning predictions for patient outcomes in electronic health records


Mar 14, 2019
William La Cava, Christopher Bauer, Jason H. Moore, Sarah A Pendergrass

* 10 pages, 5 figures, submitted to AMIA Symposium 

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Learning concise representations for regression by evolving networks of trees


Oct 05, 2018
William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore

* 16 pages, 11 figures (including Appendix) 

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EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover


Jul 26, 2018
Patryk Orzechowski, Moshe Sipper, Xiuzhen Huang, Jason H. Moore

* 9 pages, 7 figures 

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EBIC: an open source software for high-dimensional and big data biclustering analyses


Jul 26, 2018
Patryk Orzechowski, Jason H. Moore

* 2 pages, 1 figure 

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Where are we now? A large benchmark study of recent symbolic regression methods


Jun 07, 2018
Patryk Orzechowski, William La Cava, Jason H. Moore

* 8 pages, 4 figures. GECCO 2018 

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A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection


Apr 29, 2018
William La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore

* 30 pages, 8 figures. To appear in Evolutionary Computation Journal 

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Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining


Apr 03, 2018
Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore

* Revised submission to JBI 

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Relief-Based Feature Selection: Introduction and Review


Apr 02, 2018
Ryan J. Urbanowicz, Melissa Meeker, William LaCava, Randal S. Olson, Jason H. Moore

* Submitted revisions for publication based on reviews by the Journal of Biomedical Informatics 

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Data-driven Advice for Applying Machine Learning to Bioinformatics Problems


Jan 07, 2018
Randal S. Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore

* 12 pages, 5 figures, 4 tables. To be published in the proceedings of PSB 2018. Randal S. Olson and William La Cava contributed equally as co-first authors 

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Investigating the Parameter Space of Evolutionary Algorithms


Oct 10, 2017
Moshe Sipper, Weixuan Fu, Karuna Ahuja, Jason H. Moore

* BioData Mining, 2018, 11:2 

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Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure


Oct 09, 2017
Alena Orlenko, Jason H. Moore, Patryk Orzechowski, Randal S. Olson, Junmei Cairns, Pedro J. Caraballo, Richard M. Weinshilboum, Liewei Wang, Matthew K. Breitenstein

* Pacific Symposium on Biocomputing, 2018 (Vol. 23) 
* Manuscript - containing supplementary information - accepted (9/15/2017) for publication within Pacific Symposium on Biocomputing 2018 . Original supplementary information includes an additional 6 pages of content (18 pages total) and 8 figures (13 figures total) 

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A System for Accessible Artificial Intelligence


Aug 10, 2017
Randal S. Olson, Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore

* 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 workshop 

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Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods


Aug 03, 2017
William La Cava, Jason H. Moore

* Genetic and Evolutionary Computation Conference (GECCO) 2017, Berlin, Germany 

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PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison


Mar 01, 2017
Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore

* 14 pages, 5 figures, submitted for review to JMLR 

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Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming


Feb 06, 2017
Andrew Sohn, Randal S. Olson, Jason H. Moore

* 9 pages, 4 figures, submitted to GECCO 2017 conference and currently under review 

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Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool


Jul 29, 2016
Randal S. Olson, Jason H. Moore

* 13 pages, 5 figures, preprint of chapter to appear in GPTP 2016 book 

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