Table 2.

Definition and characterization of resampling techniques.

Sl. No.	Resample technique	Key features	Pros and cons
1	Random Over-Sampling	•Randomly select and replace examples from the minority class and add them to the training dataset. •Oversampling introduces duplicate samples.	•No information loss. •Chances of overfitting are high •Gradually slow down the training.
2	Random undersampling	•Removes number of samples. •Cause the model to lose out on learning essential concepts.	•Reduces the run time of the model.
3	SMOTE	•Generates synthetic samples for the minority class.	•Overfitting is reduced. •No information loss. •The efficiency of the high-dimensional data is low.
4	SMOTE-ADASYN	•A hybrid version of SMOTE.
5	SMOTE-Tomek	•A Hybrid technique of SMOTE. •Clean overlapping data points for each of the classes.	•Links the opposite class paired samples that are the closest neighbors to each other.
6	SMOTE- ENN	•Hybrid technique. •Observations are removed from the sample space.	•Utilizes ENN where the nearest neighbors of each majority class are estimated.