UBL: An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks
Version 0.0.5

Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.

Browse man pages Browse package API and functions Browse package files

AuthorPaula Branco [aut, cre], Rita Ribeiro [aut, ctb], Luis Torgo [aut, ctb]
Date of publication2016-07-13 16:17:09
MaintainerPaula Branco <paobranco@gmail.com>
LicenseGPL (>= 2)
Version0.0.5
URL https://github.com/paobranco/UBL
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("UBL")

Man pages

CNNClassif: Condensed Nearest Neighbors strategy for multiclass...
ENNClassif: Edited Nearest Neighbor for multiclass imbalanced problems
gaussNoiseClassif: Introduction of Gaussian Noise for the generation of...
gaussNoiseRegress: Introduction of Gaussian Noise for the generation of...
ImbC: Synthetic Imbalanced Data Set for a Multi-class Task
ImbR: Synthetic Regression Data Set
ImpSampClassif: Importance Sampling algorithm for imbalanced classification...
ImpSampRegress: Importance Sampling algorithm for imbalanced regression...
NCLClassif: Neighborhood Cleaning Rule (NCL) algorithm for multiclass...
OSSClassif: One-sided selection strategy for handling multiclass...
phi: Relevance function.
phiControl: Estimation of parameters used for obtaining the relevance...
randOverClassif: Random over-sampling for imbalanced classification problems
randOverRegress: Random over-sampling for imbalanced regression problems
randUnderClassif: Random under-sampling for imbalanced classification problems
randUnderRegress: Random under-sampling for imbalanced regression problems
smoteClassif: SMOTE algorithm for unbalanced classification problems
smoteRegress: SMOTE algorithm for imbalanced regression problems
TomekClassif: Tomek links for imbalanced classification problems
UBL-package: UBL: Utility-Based Learning

Functions

CNNClassif Man page Source code
ENNClassif Man page Source code
GaussNoiseClassif Man page Source code
GaussNoiseRegress Man page Source code
Gn.exsClassif Source code
Gn.exsRegress Source code
ImbC Man page
ImbR Man page
ImpSampClassif Man page Source code
ImpSampRegress Man page Source code
NCLClassif Man page Source code
OSSClassif Man page Source code
RandOverClassif Man page Source code
RandOverRegress Man page Source code
RandUnderClassif Man page Source code
RandUnderRegress Man page Source code
Smote.exsClassif Source code
Smote.exsRegress Source code
SmoteClassif Man page Source code
SmoteRegress Man page Source code
TomekClassif Man page Source code
UBL-package Man page
class.freq Source code
neighbours Source code
onUnload Source code
phi Man page Source code
phi.control Man page Source code
phi.extremes Source code
phi.range Source code
phi.setup Source code

Files

inst
inst/CITATION
tests
tests/testthat.R
tests/testthat
tests/testthat/testSmoteClassif.R
src
src/neighbours.f90
src/phi.f90
NAMESPACE
data
data/ImbR.rda
data/ImbC.rda
R
R/gaussNoiseRegress.R
R/ImpSampClassif.R
R/Neighbours.R
R/phiFunc.R
R/randUnderClassif.R
R/TomekClassif.R
R/gaussNoiseClassif.R
R/smoteClassif.R
R/ImpSampRegress.R
R/randOverClassif.R
R/CNNClassif.R
R/smoteRegress.R
R/NCLClassif.R
R/OSSClassif.R
R/zzz.R
R/ENNClassif.R
R/CallFPhi.R
R/randOverRegress.R
R/randUnderRegress.R
MD5
DESCRIPTION
man
man/OSSClassif.Rd
man/ImpSampRegress.Rd
man/smoteRegress.Rd
man/randUnderClassif.Rd
man/UBL-package.Rd
man/randUnderRegress.Rd
man/CNNClassif.Rd
man/ImbR.Rd
man/TomekClassif.Rd
man/smoteClassif.Rd
man/randOverClassif.Rd
man/gaussNoiseClassif.Rd
man/gaussNoiseRegress.Rd
man/ImbC.Rd
man/NCLClassif.Rd
man/ImpSampClassif.Rd
man/phi.Rd
man/randOverRegress.Rd
man/phiControl.Rd
man/ENNClassif.Rd
CHANGES
UBL documentation built on May 29, 2017, 12:02 p.m.