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 <email@example.com>|
|License||GPL (>= 2)|
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