TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning
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[1] Matthias Reif. A Comprehensive Dataset for Evaluating Approaches of Various Meta-learning Tasks , 2012, ICPRAM.
[2] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[3] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[4] Marc Parizeau,et al. DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..
[5] C. Chui,et al. Article in Press Applied and Computational Harmonic Analysis a Randomized Algorithm for the Decomposition of Matrices , 2022 .
[6] Wolfgang Banzhaf,et al. Genetic Programming: An Introduction , 1997 .
[7] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[8] Jason H. Moore,et al. An Expert Knowledge-Guided Mutation Operator for Genome-Wide Genetic Analysis Using Genetic Programming , 2007, PRIB.
[9] Mark Johnston,et al. Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data , 2013, IEEE Transactions on Evolutionary Computation.
[10] Randal S. Olson,et al. PMLB: a large benchmark suite for machine learning evaluation and comparison , 2017, BioData Mining.
[11] P. Simon. Too Big to Ignore: The Business Case for Big Data , 2013 .
[12] Scott M. Williams,et al. A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction , 2007, Genetic epidemiology.
[13] Aaron Klein,et al. Efficient and Robust Automated Machine Learning , 2015, NIPS.
[14] Frank Hutter,et al. Initializing Bayesian Hyperparameter Optimization via Meta-Learning , 2015, AAAI.
[15] Jason H. Moore,et al. GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures , 2012, BioData Mining.
[16] Randal S. Olson,et al. Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science , 2016, GECCO.
[17] Jaume Bacardit. Applications of evolutionary computation: 19th European conference, Evoapplications 2016 Porto, Portugal, March 30 – April 1, 2016 proceedings, part II , 2016 .
[18] Randal S. Olson,et al. Automating Biomedical Data Science Through Tree-Based Pipeline Optimization , 2016, EvoApplications.
[19] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[20] Gisele Bennett,et al. Multiple Objective Vector-Based Genetic Programming Using Human-Derived Primitives , 2015, GECCO.
[21] Lars Schmidt-Thieme,et al. Beyond Manual Tuning of Hyperparameters , 2015, KI - Künstliche Intelligenz.