Mlr: Machine learning in R

Journal of Machine Learning Research

Volumn 17, Issue , 2016, Pages

  Bischl, Bernd ^a   Lang, Michel ^a   Kotthoff, Lars ^a   Schiffner, Julia ^a   Richter, Jakob ^a   Studerus, Erich ^a   Casalicchio, Giuseppe ^a   Jones, Zachary M ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

Benchmarking; Data mining; Feature selection; Hyperparameter tuning; Machine learning; Model selection; R; Visualization

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; DATA MINING; DATA VISUALIZATION; FEATURE EXTRACTION; FLOW VISUALIZATION; LEARNING SYSTEMS;

ENSEMBLE CONSTRUCTION; EXTENSIBLE FRAMEWORK; HIGH PERFORMANCE COMPUTING; HYPER-PARAMETER; MODEL SELECTION; MODEL SELECTION TECHNIQUES; STRUCTURED ENVIRONMENT; SURVIVAL ANALYSIS;

LEARNING ALGORITHMS;

EID: 84995400210     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. B. Bischl and M. Lang. parallelMap: Unified interface to some popular parallelization back- ends for interactive usage and package development, 2015. URL https://github.com/berndbischl/parallelMap. R package version 1.3.
2. B. Bischl, M. Lang, O. Mersmann, J. Rahnenführer, and C. Weihs. BatchJobs and Batch- Experiments: Abstraction mechanisms for using R in batch environments. Journal of Statistical Software, 64(11), 2015.
3. P. Cortez. Data Mining with Neural Networks and Support Vector Machines using the R/rminer Tool. In P. Perner, editor, Advances in Data Mining. Applications and Theoretical Aspects, volume 6171 of LNCS, pages 572-583, Berlin, Germany, 2010. Springer.
4. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA data mining software: An update. SIGKDD Explorations, 11(1), 2009.
5. P. Koch, B. Bischl, O. Flasch, T. Bartz-Beielstein, C. Weihs, and W. Konen. Tuning and evolution of support vector kernels. Evolutionary Intelligence, 5(3): 153-170, 2012.
6. M. Kuhn. Building predictive models in R using the caret package. Journal of Statistical Software, 28(5): 1-26, 2008.
7. M. Lang, H. Kotthaus, P. Marwedel, C. Weihs, J. Rahnenführer, and B. Bischl. Automatic model selection for high-dimensional survival analysis. Journal of Statistical Computation and Simulation, 85(1): 62-76, 2015.
8. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12: 2825-2830, 2011.
9. A. Peters and T. Hothorn. ipred: Improved Predictors, 2015. URL http://CRAN. R-project.org/package=ipred. R package version 0.9-5.
10. M. Robnik-Sikonja and P. S. with contributions from John Adeyanju Alao. CORE- learn: Classification, Regression and Feature Evaluation, 2016. URL https://CRAN. R-project.org/package=CORElearn. R package version 1.48.0.
11. J. Schiner, B. Bischl, M. Lang, J. Richter, Z. M. Jones, P. Probst, F. Psterer, M. Gallo, D. Kirchho, T. Kühn, J. Thomas, and L. Kotthoff. mlr tutorial, 2016.
12. M. Slawski, M. Daumer, and A.-L. Boulesteix. CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics, 9 (1): 439, 2008.
13. L. Torgo. Data Mining with R: Learning with Case Studies. Data Mining and Knowledge Discovery Series. Chapman and Hall/CRC, Boca Raton, FL, 2010.
14. J. Vanschoren, J. N. van Rijn, B. Bischl, and L. Torgo. OpenML: Networked science in machine learning. SIGKDD Explorations, 15(2): 49-60, 2013.
15. G. J. Williams. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. Use R! Springer, New York, NY, 2011.