A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE
Adaptive Synthetic Sampling Approach for Imbalanced Learning
Adaptive Neighbor Synthetic Majority Oversampling TEchnique
Borderline-SMOTE
Density-based SMOTE
The function to provide a random number which is used as a location of...
Counting the number of each class in K nearest neighbor
The function to find n_clust nearest neighbors of each instance, alway...
The function to calculate the maximum round each sampling is repeated
Relocating Safe-level SMOTE
The function to generate 2-dimensional dataset
Safe-level SMOTE
Synthetic Minority Oversampling TEchnique
SMOTE family package for Data Generation
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. Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.