MultiDimensional Feature Selection

Functions for MultiDimensional Feature Selection (MDFS): calculating multidimensional information gains, scoring variables, finding important variables, plotting selection results. This package includes an optional CUDA implementation that speeds up information gain calculation using NVIDIA GPGPUs. R. Piliszek et al. (2019) .


1.0.3 | 2018-11-07

  • add note about recommended FDR control method

  • fix a possible memory error introduced in 1.0.2 (mismatched new/delete[] operators)

1.0.2 | 2018-10-31

  • improve default range estimation

  • allow overriding pseudo.count in MDFS function

  • set default p.adjust.method to default p.adjust method ("holm")

  • set default level to suggested FWER level (0.05)

  • fix CUDA version to not abruptly exit R on error

  • fix use.CUDA=T in MDFS function

1.0.1 | 2018-06-26

  • fix factors as input decision in ComputeMaxInfoGains, ComputeInterestingTuples and MDFS

  • fix adjusted.p.value in MDFS

  • return statistic and p.value in MDFS

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.2.0 by Radosław Piliszek, 8 months ago

Browse source code at

Authors: Radosław Piliszek [aut, cre] , Krzysztof Mnich [aut] , Paweł Tabaszewski [aut] , Szymon Migacz [aut] , Andrzej Sułecki [aut] , Witold Remigiusz Rudnicki [aut]

Documentation:   PDF Manual  

GPL-3 license

System requirements: C++11

See at CRAN