An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks
ADASYN algorithm for unbalanced classification problems, both binary a...
Standard Bagging ensemble for regression problems.
Class "BagModel"
Condensed Nearest Neighbors strategy for multiclass imbalanced problem...
Distance matrix between all data set examples according to a selected ...
Edited Nearest Neighbor for multiclass imbalanced problems
Utility metrics for assessing the performance of utility-based classif...
Utility metrics for assessing the performance of utility-based regress...
Introduction of Gaussian Noise for the generation of synthetic example...
Introduction of Gaussian Noise for the generation of synthetic example...
WEighted Relevance-based Combination Strategy (WERCS) algorithm for im...
WEighted Relevance-based Combination Strategy (WERCS) algorithm for im...
Neighborhood Cleaning Rule (NCL) algorithm for multiclass imbalanced p...
Computation of nearest neighbours using a selected distance function.
One-sided selection strategy for handling multiclass imbalanced proble...
Relevance function.
Estimation of parameters used for obtaining the relevance function.
Predicting on new data with a BagModel model
Random over-sampling for imbalanced classification problems
Random over-sampling for imbalanced regression problems
Random under-sampling for imbalanced classification problems
Random under-sampling for imbalanced regression problems
REBaggRegress: RE(sampled) BAG(ging), an ensemble method for dealing w...
SMOGN algorithm for imbalanced classification problems
SMOGN algorithm for imbalanced regression problems
SMOTE algorithm for unbalanced classification problems
SMOTE algorithm for imbalanced regression problems
Tomek links for imbalanced classification problems
UBL: Utility-Based Learning
Utility surface obtained through methods for spatial interpolation of ...
Optimization of predictions utility, cost or benefit for classificatio...
Optimization of predictions utility, cost or benefit for regression pr...
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.