Is there any limitation on the data ratio that is not suitable for cross-validation?
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I need to answer the reviewers' cross-validation after submitting my article to the Q1 journal. The scenario is that I did work on a hypothesis-based model and performed on linear classifiers. However, I also used augmentation techniques due to the scarcity of the original datasets (ratio in percentage 87:13), including a modified-augmentation technique. Overall, my article has novelty and a contribution, but I did not use cross-validation and testing due to the imbalanced dataset. I have extracted the relevant features using the filter and wrapper methods and shown the results on a training dataset.
Can anyone suggest a way to convince the reviewer why I did not use any cross-validation with appropriate logic? Is there any reference in which we could not perform cross-validation on a highly imbalanced dataset, even if augmentation techniques are used?