The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Random Forests and Kernel Methods
Open accessAbstract
Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high-dimensional settings. On the theoretical side, several studies highlight the potentially fruitful connection between the random forests and the kernel methods. In this paper, we work out this connection in detail. In particular, we show that by slightly modifying their definition, random forests can be rewritten as kernel methods (called KeRF for kernel based on random forests) which are more interpretable and easier to analyze. Explicit expressions of KeRF estimates for some specific random forest models are given, together with upper bounds on their rate of consistency. We also show empirically that the KeRF estimates compare favourably to the random forest estimates.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,522
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 20,825
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300
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A Distribution-Free Theory of Nonparametric Regression
Györfi, L., Kohler, M., Krzyżak, A., & Walk, H. (2002) · Springer Series in Statistics · doi:10.1007/b97848 · Cited by 1,738
van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1309 · Cited by 1,719
Random Forest for Bioinformatics
Qi, Y. (2012) · Ensemble Machine Learning · doi:10.1007/978-1-4419-9326-7_11 · Cited by 754
Cited by
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149
Self-citationA multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks
Muzammal, M., Talat, R., Sodhro, A. H., & Pirbhulal, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.06.021 · Cited by 311
Metadata
- Authors
- Scornet, E.
- Venue
- IEEE Transactions on Information Theory
- Published
- 2016-03
- Volume
- 62
- Issue
- 3
- Pages
- 1485-1500
- ISSN
- 0018-9448 (ISSN-L)
- 1557-9654
- OpenAlex
- W1875028359
- Cited by
- 296
- References
- 33
- FWCI
- 13.96
- Type
- journal article
- Language
- en
- Open access
- green
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