# 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 No Results!