smotefamily: 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.
- Wacharasak Siriseriwan [aut, cre]
- Date of publication
- 2016-09-08 07:33:52
- Wacharasak Siriseriwan <email@example.com>
- Adaptive Synthetic Sampling Approach for Imbalanced Learning
- Adaptive Neighbor Synthetic Majority Oversampling TEchnique
- Density-based SMOTE
- The function to provide a random number which is used as a...
- Counting the number of each class in K nearest neighbor
- The function to find n_clust nearest neighbors of each...
- The function to calculate the maximum round each sampling is...
- Relocating Safe-level SMOTE
- The function to generate 2-dimensional dataset
- Safe-level SMOTE
- Synthetic Minority Oversampling TEchnique
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