smotefamily v1.3.1

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

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.

Functions in smotefamily

Name Description
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
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Date 2019-05-30
License GPL (>= 3)
NeedsCompilation no
Packaged 2019-05-30 06:56:36 UTC; Dew
Repository CRAN
Date/Publication 2019-05-30 07:30:02 UTC
imports , dbscan , FNN , igraph
depends R (>= 3.0.0)
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