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I am trying to make some semantic segmentation. I have 7 imbalanced classes in my case. I found several methods for handling Class Imbalance in a dataset is to perform Undersampling for the Majority Classes or Oversampling for the minority classes. but the most used one is introducing weights in the Loss Function. And I found several formula to calculate weights such us: wj=n_samples / (n_classes * n_samplesj) or wj=1/n_samplesj
which is the best one?
I really don't suggest Under/Oversampling as it would change the distribution of dataset. we should consider distribution as a useful feature of dataset. so I think the weighted loss would have better performance in most cases. if you're using TF/Keras, this link would be useful. you can use a variety of loss functions, like the below one, to apply the weight.
tf.nn.weighted_cross_entropy_with_logits(
labels, logits, pos_weight, name=None
)
A value pos_weight > 1 decreases the false negative count, hence increasing the recall. Conversely setting pos_weight < 1 decreases the false positive count and increases the precision.
w_j=n_samples / (n_classes * n_samples_j). but you can think of it as a hyperparameter so using the resulted weights, you might still need some tests and trials to find the best values for weights in order to get the highest accuracy.
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May 2, 2021 at 18:42