The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Correlation and variable importance in random forests
Open accessAbstract
This paper is about variable selection with the random forests algorithm in presence of correlated predictors. In high-dimensional regression or classification frameworks, variable selection is a difficult task, that becomes even more challenging in the presence of highly correlated predictors. Firstly we provide a theoretical study of the permutation importance measure for an additive regression model. This allows us to describe how the correlation between predictors impacts the permutation importance. Our results motivate the use of the recursive feature elimination (RFE) algorithm for variable selection in this context. This algorithm recursively eliminates the variables using permutation importance measure as a ranking criterion. Next various simulation experiments illustrate the efficiency of the RFE algorithm for selecting a small number of variables together with a good prediction error. Finally, this selection algorithm is tested on the Landsat Satellite data from the UCI Machine Learning Repository.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
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Gene selection for cancer classification using support vector machines
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Variable selection using random forests
Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.03.014 · Cited by 2,619
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Cited by
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149
Fisher, A., Rudin, C., & Dominici, F. (2018) · arXiv · doi:10.48550/arxiv.1801.01489 · Cited by 1,165
Fake News Detection Using Machine Learning Ensemble Methods
Ahmad, I., Yousaf, M., Yousaf, S., & Ahmad, M. O. (2020) · Complexity · doi:10.1155/2020/8885861 · Cited by 474
Machine Learning Predicts Laboratory Earthquakes
Rouet‐Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C. J., & Johnson, P. A. (2017) · Geophysical Research Letters · doi:10.1002/2017gl074677 · Cited by 420
Random forests for global sensitivity analysis: A selective review
Antoniadis, A., Lambert-Lacroix, S., & Poggi, J.-M. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107312 · Cited by 363
Metadata
- Authors
- Gregorutti, B., Michel, B., & Saint-Pierre, P.
- Venue
- Statistics and Computing
- Published
- 2016-03-23
- Volume
- 27
- Issue
- 3
- Pages
- 659-678
- ISSN
- 0960-3174 (ISSN-L)
- 1573-1375
- OpenAlex
- W2169281690
- Cited by
- 1,073
- References
- 6
- FWCI
- 37.33
- Type
- journal article
- Language
- en
- Open access
- green
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