Boruta: A wrapper algorithm for all-relevant feature selection. Boruta is an all-relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes’ importance with importance achievable at random, estimated using their permuted copies.
Keywords for this software
References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Thomas, Janek; Hepp, Tobias; Mayr, Andreas; Bischl, Bernd: Probing for sparse and fast variable selection with model-based boosting (2017)
- Nguyen, Thanh-Tung; Huang, Joshua Z.; Nguyen, Thuy Thi: Two-level quantile regression forests for bias correction in range prediction (2015)
- Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
- Deng, Houtao; Runger, George: Gene selection with guided regularized random forest (2013) ioport
- Barb, Adrian S.: Gaussian mixture models for semantic ranking in domain specific databases with application in radiology (2011) ioport
- Kursa, Miron B.; Jankowski, Aleksander; Rudnicki, Witold R.: Boruta -- a system for feature selection (2010) ioport
- Miron Kursa; Witold Rudnicki: Feature Selection with the Boruta Package (2010) not zbMATH