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

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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.

Author
Paula Branco [aut, cre], Rita Ribeiro [aut, ctb], Luis Torgo [aut, ctb]
Date of publication
2016-07-13 16:17:09
Maintainer
Paula Branco <paobranco@gmail.com>
License
GPL (>= 2)
Version
0.0.5
URLs

View on CRAN

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

Files in this package

UBL
UBL/inst
UBL/inst/CITATION
UBL/tests
UBL/tests/testthat.R
UBL/tests/testthat
UBL/tests/testthat/testSmoteClassif.R
UBL/src
UBL/src/neighbours.f90
UBL/src/phi.f90
UBL/NAMESPACE
UBL/data
UBL/data/ImbR.rda
UBL/data/ImbC.rda
UBL/R
UBL/R/gaussNoiseRegress.R
UBL/R/ImpSampClassif.R
UBL/R/Neighbours.R
UBL/R/phiFunc.R
UBL/R/randUnderClassif.R
UBL/R/TomekClassif.R
UBL/R/gaussNoiseClassif.R
UBL/R/smoteClassif.R
UBL/R/ImpSampRegress.R
UBL/R/randOverClassif.R
UBL/R/CNNClassif.R
UBL/R/smoteRegress.R
UBL/R/NCLClassif.R
UBL/R/OSSClassif.R
UBL/R/zzz.R
UBL/R/ENNClassif.R
UBL/R/CallFPhi.R
UBL/R/randOverRegress.R
UBL/R/randUnderRegress.R
UBL/MD5
UBL/DESCRIPTION
UBL/man
UBL/man/OSSClassif.Rd
UBL/man/ImpSampRegress.Rd
UBL/man/smoteRegress.Rd
UBL/man/randUnderClassif.Rd
UBL/man/UBL-package.Rd
UBL/man/randUnderRegress.Rd
UBL/man/CNNClassif.Rd
UBL/man/ImbR.Rd
UBL/man/TomekClassif.Rd
UBL/man/smoteClassif.Rd
UBL/man/randOverClassif.Rd
UBL/man/gaussNoiseClassif.Rd
UBL/man/gaussNoiseRegress.Rd
UBL/man/ImbC.Rd
UBL/man/NCLClassif.Rd
UBL/man/ImpSampClassif.Rd
UBL/man/phi.Rd
UBL/man/randOverRegress.Rd
UBL/man/phiControl.Rd
UBL/man/ENNClassif.Rd
UBL/CHANGES