can we combine supervised and unsupervised filters in weka in data preprocessing? Is it a good choice to use SMOTE from supervised and Resample from unsupervised filters in WEKA for achieving a high accuracy?
where are under sampling and oversampling filters? and their exact names in WEKA?
it is a good suggestion, it will be incorporated as soon as possible.
Class imbalanced data should be treated with precautions, we proved that many SMOTE variants methods provide fake examples to increase the performance of the system on paper, but in reality, it is a different story because most of these synthesized examples are majority examples and forced to be minority due to their similarities, please find our new study at:
https://ieeexplore.ieee.org/document/9761871
Oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.
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