| Autor: |
Michał Dramiński, Jacek Koronacki |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Journal of Statistical Software, Vol 85, Iss 1, Pp 1-28 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1548-7660 |
| DOI: |
10.18637/jss.v085.i12 |
| Popis: |
We describe the R package rmcfs that implements an algorithm for ranking features from high dimensional data according to their importance for a given supervised classification task. The ranking is performed prior to addressing the classification task per se. This R package is the new and extended version of the MCFS (Monte Carlo feature selection) algorithm where an early version was published in 2005. The package provides an easy R interface, a set of tools to review results and the new ID (interdependency discovery) component. The algorithm can be used on continuous and/or categorical features (e.g., gene expression and phenotypic data) to produce an objective ranking of features with a statistically well-defined cutoff between informative and non-informative ones. Moreover, the directed ID graph that presents interdependencies between informative features is provided. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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