This is a hybrid intelligent sampling technique to tackle class-imbalance in real world datasets. To know more about it please follow the link: https://www.sciencedirect.com/science/article/pii/S1568494619300924. Cite our paper, if you are using it in your research work.
The citation of our paper is:
[1] Susan, Seba, and Amitesh Kumar. "SSOMaj-SMOTE-SSOMin: Three-step intelligent pruning of majority and minority samples for learning from imbalanced datasets." Applied Soft Computing 78 (2019): 141-149.
For reading free PDF, you can get it at: https://www.researchgate.net/publication/331217920_SSO_Maj_-SMOTE-SSO_Min_Three-step_intelligent_pruning_of_majority_and_minority_samples_for_learning_from_imbalanced_datasets
To know more about imbalanced data, please follow this link: https://onlinelibrary.wiley.com/doi/full/10.1002/eng2.12298. The citation of this paper is:
[2] Susan, Seba, and Amitesh Kumar. "The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art." Engineering Reports 3, no. 4 (2021): e12298.
For Reading Free PDF, you can get it at: https://www.researchgate.net/publication/344781218_The_balancing_trick_Optimized_sampling_of_imbalanced_datasets-A_brief_survey_of_the_recent_State_of_the_Art
Important !!! :
This file requires *.arff extension files. At run time please specify the directory path for input file. The output file will be created in same directory. Please include weka jar files or weka_related_jar.jar (which is a weka jar itself) in your Referenced libraries (in case of Eclipse IDE).