Editing Random forest (section)

===Random forests for high-dimensional data===
The basic random forest procedure may not work well in situations where there are a large number of features but only a small proportion of these features are informative with respect to sample classification. This can be addressed by encouraging the procedure to focus mainly on features and trees that are informative. Some methods for accomplishing this are:

* Prefiltering: Eliminate features that are mostly just noise.<ref>Dessi, N. & Milia, G. & Pes, B. (2013). Enhancing random forests performance in microarray data classification. Conference paper, 99-103. 10.1007/978-3-642-38326-7_15.</ref><ref>Ye, Y., Li, H., Deng, X., and Huang, J. (2008) Feature weighting random forest for detection of hidden web search interfaces. Journal of Computational Linguistics and Chinese Language Processing, 13, 387–404.</ref>
* Enriched random forest (ERF): Use weighted random sampling instead of simple random sampling at each node of each tree, giving greater weight to features that appear to be more informative.<ref>Amaratunga, D., Cabrera, J., Lee, Y.S. (2008) Enriched Random Forest. Bioinformatics, 24, 2010-2014.</ref><ref>Ghosh D, Cabrera J. (2022) Enriched random forest for high dimensional genomic data. IEEE/ACM Trans Comput Biol Bioinform. 19(5):2817-2828. doi:10.1109/TCBB.2021.3089417. </ref>
* Tree-weighted random forest (TWRF): Give more weight to more accurate trees.<ref>Winham, Stacey & Freimuth, Robert & Biernacka, Joanna. (2013). A weighted random forests approach to improve predictive performance. Statistical Analysis and Data Mining. 6. 10.1002/sam.11196. </ref><ref> Li, H. B., Wang, W., Ding, H. W., & Dong, J. (2010, 10-12 Nov. 2010). Trees weighting random forest method for classifying high-dimensional noisy data. Paper presented at the 2010 IEEE 7th International Conference on E-Business Engineering. </ref>