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

AdasynClassifADASYN algorithm for unbalanced classification problems, both...
CNNClassifCondensed Nearest Neighbors strategy for multiclass...
DistancesDistance matrix between all data set examples according to a...
ENNClassifEdited Nearest Neighbor for multiclass imbalanced problems
EvalClassifMetricsUtility metrics for assessing the performance of...
EvalRegressMetricsUtility metrics for assessing the performance of...
gaussNoiseClassifIntroduction of Gaussian Noise for the generation of...
gaussNoiseRegressIntroduction of Gaussian Noise for the generation of...
ImbCSynthetic Imbalanced Data Set for a Multi-class Task
ImbRSynthetic Regression Data Set
ImpSampClassifImportance Sampling algorithm for imbalanced classification...
ImpSampRegressImportance Sampling algorithm for imbalanced regression...
NCLClassifNeighborhood Cleaning Rule (NCL) algorithm for multiclass...
neighboursComputation of nearest neighbours using a selected distance...
OSSClassifOne-sided selection strategy for handling multiclass...
phiRelevance function.
phiControlEstimation of parameters used for obtaining the relevance...
randOverClassifRandom over-sampling for imbalanced classification problems
randOverRegressRandom over-sampling for imbalanced regression problems
randUnderClassifRandom under-sampling for imbalanced classification problems
randUnderRegressRandom under-sampling for imbalanced regression problems
smoteClassifSMOTE algorithm for unbalanced classification problems
smoteRegressSMOTE algorithm for imbalanced regression problems
TomekClassifTomek links for imbalanced classification problems
UBL-packageUBL: Utility-Based Learning
UtilInterpolUtility surface obtained through methods for spatial...
UtilOptimClassifOptimization of predictions utility, cost or benefit for...
UtilOptimRegressOptimization of predictions utility, cost or benefit for...
UBL documentation built on July 13, 2017, 5:02 p.m.