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I have an image dataset that I need to segment into directories (train, validation and test) using ImageDataGenerator in TensorFlow/Keras. The dataset is highly imbalanced: enter image description here For this I have decided to do the following in the training set: under-sample the majority class and augment the minority class images. However, this under-sampling would result in the loss of information.

Is there any way to quantify the extent of loss as a result of under-sampling the majority class, in terms of its share in the training set?

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  • $\begingroup$ Why would you undersample? That both changes the class frequencies to something unnatural and discards precious data. $\endgroup$
    – Dave
    Commented Nov 24, 2022 at 17:02
  • $\begingroup$ The differences between the percentages of samples in the training set for each class were not TOO big. It was 75% for the majority class vs 85% for minority classes for an eventual 80-10-10 split. However, I had to drop the idea of under-sampling. Though I am still curious if there is a way to determine the extent of information loss due to under-sampling. $\endgroup$ Commented Nov 25, 2022 at 17:46

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refer article: Entropy and improved k‐nearest neighbor search based under‐sampling (ENU) method to handle class overlap in imbalanced datasets

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