A hybrid method of feature selection which combines both filter and wrapper methods. The first level involves feature reduction based on some of the important filter methods while the second level involves feature subset selection as in a wrapper method. Comparative analysis with the existing feature selection packages shows this package results in higher classification accuracy, reduced processing time and improved data handling capacity.
| Version: | 0.1.2 |
| Depends: | R (≥ 3.4.1) |
| Imports: | FSelector, caTools, woeBinning, ROCR, InformationValue |
| Published: | 2017-10-11 |
| Author: | Yamini Pandari [aut, cre], Prashanth Thangavel [aut], Hemanth Senthamaraikannan [aut], Sivaranjani Jagadeeswaran [aut], Thirumaalavan Elumalai [aut] |
| Maintainer: | Yamini Pandari <yamini.pandari at latentview.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | HybridFS results |
| Reference manual: | HybridFS.pdf |
| Package source: | HybridFS_0.1.2.tar.gz |
| Windows binaries: | r-devel: HybridFS_0.1.2.zip, r-release: HybridFS_0.1.2.zip, r-oldrel: HybridFS_0.1.2.zip |
| OS X binaries: | r-release: HybridFS_0.1.2.tgz, r-oldrel: HybridFS_0.1.2.tgz |
| Old sources: | HybridFS archive |
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