How to balance a PyTorch dataset?

Question

I have an imbalanced PyTorch dataset. The number of A and V samples is much lower than the others I would like to ballanase my dataset, even if I have to delete samples that belong to the prevailing class. How can do it?

Now I just remove samples of certain classes if their number exceeds some fixed value. This is technically complicated and not convenient. Maybe there is some sklearn or PyTorch method that makes this algorithm much easier to implement?

Answer 1

Removing samples from the prevailing classes is not a recommended strategy:

1. Loss of important information,
2. Might be incur bias in the model towards the minority classes.

Instead, there are several strategies you can use to balance the dataset, including:

1. Oversampling: Generating new samples for the minority classes to increase their representation in the dataset. This can be done through techniques such as:

   a. Synthetic Minority Over-sampling Technique (SMOTE)
   b. Adaptive Synthetic Sampling (ADASYN).

2. Under-sampling (which you are doing): Reducing the number of samples for the majority classes to match the number of samples for the minority classes. This can be done through techniques such as:

   a. Random Undersampling
   b. Tomek Links.

3. Combination of oversampling and under-sampling: This involves using a combination of oversampling and under-sampling techniques to balance the dataset.

There are several methods available in both PyTorch to help balance the dataset:

1. WeightedRandomSampler: This sampler allows you to specify weights for each class, which can be used to oversample the minority classes or undersample the majority classes.
2. DataLoader: This class provides several options for shuffling and batching the data, which can help ensure that each batch contains a balanced representation of the classes.