Name
Abstract | ||
|---|---|---|
Random Forests are frequently applied as they achieve a high prediction accuracy and have the ability to identify informative variables. Several approaches for variable selection have been proposed to combine and intensify these qualities. An extensive review of the corresponding literature led to the development of a new approach that is based on the theoretical framework of permutation tests and meets important statistical properties. A comparison to another eight popular variable selection methods in three simulation studies and four real data applications indicated that: the new approach can also be used to control the test-wise and family-wise error rate, provides a higher power to distinguish relevant from irrelevant variables and leads to models which are located among the very best performing ones. In addition, it is equally applicable to regression and classification problems. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.csda.2012.09.020 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
new variable selection approach,extensive review,family-wise error rate,irrelevant variable,random forests,corresponding literature,classification problem,new approach,informative variable,variable selection,popular variable selection method,multiple testing | Econometrics,Feature selection,Regression,Permutation,Word error rate,Multiple comparisons problem,Artificial intelligence,Statistics,Random forest,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
60, | 0167-9473 | 26 |
PageRank | References | Authors |
1.16 | 24 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Hapfelmeier | 1 | 77 | 5.52 |
| K. Ulm | 2 | 33 | 1.77 |