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The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study.
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza · 1992
Earlier work this paper cites.
Noise-tolerant Learning, the Parity Problem, and the Statistical Query Model
Avrim Blum, Adam Kalai, and Hal Wasserman · 2003
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Spectral biclustering of microarray data: coclustering genes and conditions
Yuval Kluger, Ronen Basri, Joseph T Chang, and Mark Gerstein · 2003
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Machine learning benchmarks and random forest regression
Mark R Segal · 2004
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An empirical comparison of supervised learning algorithms
Rich Caruana and Alexandru Niculescu-Mizil · 2006
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A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility
Jason H Moore, Joshua C Gilbert, Chia-Ti Tsai, Fu-Tien Chiang, Todd Holden, Nate Barney, and Bill C White · 2006
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A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction
Digna R Velez et al · 2007
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor J. Hastie, Robert John Tibshirani, and Jerome H. Friedman · 2009
Cited alongside, same era.
Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework
J Alcalá, A Fernández, J Luengo, J Derrac, S García, L Sánchez, and F Herrera · 2010
Cited alongside, same era.
Kaggle: Your home for data science, 2010
Anthony Goldbloom · 2010
Cited alongside, same era.
Open issues in genetic programming
Michael O’Neill, Leonardo Vanneschi, Steven Gustafson, and Wolfgang Banzhaf · 2010
Cited alongside, same era.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
Genetic programming needs better benchmarks
Creating and benchmarking a new dataset for physical activity monitoring
Attila Reiss and Didier Stricker · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
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UCI machine learning repository, 2013
M. Lichman · 2013
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Better gp benchmarks: community survey results and proposals
David R. White, James McDermott, Mauro Castelli, Luca Manzoni, Brian W. Goldman, Gabriel Kronberger, Wojciech Jaśkowski, Una-May O’Reilly, and Sean Luke · 2013
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Exstracs 2.0: description and evaluation of a scalable learning classifier system
Ryan J Urbanowicz and Jason H Moore · 2015
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James McDermott, David R. White, Sean Luke, Luca Manzoni, Mauro Castelli, Leonardo Vanneschi, Wojciech Jaskowski, Krzysztof Krawiec, Robin Harper, Kenneth De Jong, and Una-May O’Reilly · 2012
Cited alongside, same era.
A comprehensive dataset for evaluating approaches of various meta-learning tasks
Matthias Reif · 2012
Cited alongside, same era.
URL http://pandas.pydata.org/
Pandas: Python data analysis library
Cited in the paper.
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection
Ryan J Urbanowicz, Jeff Kiralis, Jonathan M Fisher, and Jason H Moore
Cited in the paper.
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, and Jason H Moore
Cited in the paper.
Inference of compact nonlinear dynamic models by epigenetic local search
William La Cava, Kourosh Danai, and Lee Spector · 2016
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Detecting gene-gene interactions using a permutation-based random forest method
Jing Li, James D Malley, Angeline S Andrew, Margaret R Karagas, and Jason H Moore · 2016
Later among the works it cites.