I have a dataset of 4 classes with the following number of instances:
- Class 0: 13175
- Class 1: 82
- Class 2: 75
- Class 3: 121
Have have applied several subsampling and oversampling methods from the Python imbalance-learn API but none of them had a good performance for all classes. I have applied:
- Undersampling: CondensedNearestNeighbour, EditedNearestNeighbours, NeighbourhoodCleaningRule, RandomUnderSampler.
- Oversampling: SMOTE, ADASYN
- class_weight:['balanced'] parameter option in my grid search
- costcla library, but it does not work with more than two classes.
And I was not successful. Could you suggest a solution for this problem?