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smotefamily (version 1.3.1)

A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE

Description

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 for more information) Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.

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Version

Install

install.packages('smotefamily')

Monthly Downloads

11,002

Version

1.3.1

License

GPL (>= 3)

Maintainer

Wacharasak Siriseriwan

Last Published

May 30th, 2019

Functions in smotefamily (1.3.1)

SMOTE

Synthetic Minority Oversampling TEchnique
n_dup_max

The function to calculate the maximum round each sampling is repeated
sample_generator

The function to generate 2-dimensional dataset
smotefamily-package

A short title line describing what the package does
DBSMOTE

Density-based SMOTE
kncount

Counting the number of each class in K nearest neighbor
ADASYN

Adaptive Synthetic Sampling Approach for Imbalanced Learning
ANS

Adaptive Neighbor Synthetic Majority Oversampling TEchnique
knearest

The function to find n_clust nearest neighbors of each instance, always removing the index of that instance if it is reported.
Borderline-SMOTE

Borderline-SMOTE
RSLS

Relocating Safe-level SMOTE
SLS

Safe-level SMOTE
gap

The function to provide a random number which is used as a location of synthetic instance