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UBL (version 0.0.6)

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

Description

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.

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Version

Install

install.packages('UBL')

Monthly Downloads

1,258

Version

0.0.6

License

GPL (>= 2)

Issues

10

Pull Requests

1

Stars

34

Forks

11

Maintainer

Paula Branco

Last Published

July 13th, 2017

Functions in UBL (0.0.6)

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.
ImbC

Synthetic Imbalanced Data Set for a Multi-class Task
ImbR

Synthetic Regression Data Set
AdasynClassif

ADASYN algorithm for unbalanced classification problems, both binary and multi-class.
CNNClassif

Condensed Nearest Neighbors strategy for multiclass imbalanced problems
NCLClassif

Neighborhood Cleaning Rule (NCL) algorithm for multiclass imbalanced problems
OSSClassif

One-sided selection strategy for handling multiclass imbalanced problems.
GaussNoiseRegress

Introduction of Gaussian Noise for the generation of synthetic examples to handle imbalanced regression problems
neighbours

Computation of nearest neighbours using a selected distance function.
phi

Relevance function.
phi.control

Estimation of parameters used for obtaining the relevance function.
UtilOptimRegress

Optimization of predictions utility, cost or benefit for regression problems.
GaussNoiseClassif

Introduction of Gaussian Noise for the generation of synthetic examples to handle imbalanced multiclass 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
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
ImpSampClassif

Importance Sampling algorithm for imbalanced classification problems
ImpSampRegress

Importance Sampling algorithm for imbalanced regression problems
UtilInterpol

Utility surface obtained through methods for spatial interpolation of points.
UtilOptimClassif

Optimization of predictions utility, cost or benefit for classification problems.