A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor. (see <https://www.jair.org/media/953/live-953-2037-jair.pdf> for more information) Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
|Author||Wacharasak Siriseriwan [aut, cre]|
|Date of publication||2016-09-08 07:33:52|
|Maintainer||Wacharasak Siriseriwan <firstname.lastname@example.org>|
adas: Adaptive Synthetic Sampling Approach for Imbalanced Learning
ANS: Adaptive Neighbor Synthetic Majority Oversampling TEchnique
DBSMOTE: Density-based SMOTE
gap: The function to provide a random number which is used as a...
kncount: Counting the number of each class in K nearest neighbor
knearest: The function to find n_clust nearest neighbors of each...
n_dup_max: The function to calculate the maximum round each sampling is...
RSLS: Relocating Safe-level SMOTE
sample_generator: The function to generate 2-dimensional dataset
SLS: Safe-level SMOTE
SMOTE: Synthetic Minority Oversampling TEchnique