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

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Documentation for package ‘UBL’ version 0.0.9

• DESCRIPTION file.

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UBL-package	UBL: Utility-Based Learning
AdasynClassif	ADASYN algorithm for unbalanced classification problems, both binary and multi-class.
BaggingRegress	Standard Bagging ensemble for regression problems.
BagModel	Class "BagModel"
BagModel-class	Class "BagModel"
CNNClassif	Condensed Nearest Neighbors strategy for multiclass imbalanced problems
distances	Distance matrix between all data set examples according to a selected distance metric.
ENNClassif	Edited Nearest Neighbor for multiclass imbalanced problems
EvalClassifMetrics	Utility metrics for assessing the performance of utility-based classification tasks.
EvalRegressMetrics	Utility metrics for assessing the performance of utility-based regression tasks.
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
NCLClassif	Neighborhood Cleaning Rule (NCL) algorithm for multiclass imbalanced problems
neighbours	Computation of nearest neighbours using a selected distance function.
OSSClassif	One-sided selection strategy for handling multiclass imbalanced problems.
phi	Relevance function.
phi.control	Estimation of parameters used for obtaining the relevance function.
predict-method	Predicting on new data with a *BagModel* model
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
ReBaggRegress	REBaggRegress: RE(sampled) BAG(ging), an ensemble method for dealing with imbalanced regression problems.
show-method	Class "BagModel"
SMOGNClassif	SMOGN algorithm for imbalanced classification problems
SMOGNRegress	SMOGN algorithm 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
UtilInterpol	Utility surface obtained through methods for spatial interpolation of points.
UtilOptimClassif	Optimization of predictions utility, cost or benefit for classification problems.
UtilOptimRegress	Optimization of predictions utility, cost or benefit for regression problems.
WERCSClassif	WEighted Relevance-based Combination Strategy (WERCS) algorithm for imbalanced classification problems
WERCSRegress	WEighted Relevance-based Combination Strategy (WERCS) algorithm for imbalanced regression problems