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

Documentation for package ‘UBL’ version 0.0.5

UBL-package	UBL: Utility-Based Learning
CNNClassif	Condensed Nearest Neighbors strategy for multiclass imbalanced problems
ENNClassif	Edited Nearest Neighbor for multiclass imbalanced problems
GaussNoiseClassif	Introduction of Gaussian Noise for the generation of synthetic examples to handle imbalanced multiclass problems.
GaussNoiseRegress	Introduction of Gaussian Noise for the generation of synthetic examples to handle imbalanced regression problems
ImbC	Synthetic Imbalanced Data Set for a Multi-class Task
ImbR	Synthetic Regression Data Set
ImpSampClassif	Importance Sampling algorithm for imbalanced classification problems
ImpSampRegress	Importance Sampling algorithm for imbalanced regression problems
NCLClassif	Neighborhood Cleaning Rule (NCL) algorithm for multiclass imbalanced problems
OSSClassif	One-sided selection strategy for handling multiclass imbalanced problems.
phi	Relevance function.
phi.control	Estimation of parameters used for obtaining the relevance function.
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