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I have incorporated the InfogainLossLayer as suggested by Shai. I've also added another custom layer that builds the infogain matrix H based on the imbalance in the current batch.

Currently, the matrix is configured as follows:

H(i, j) = 0          if i != j

H(i, j) = 1 - f(i)   if i == j (with f(i) = the frequency of class i in the batch)

I'm planning on experimenting with different configurations for the matrix in the future.

I have tested this on a 10:1 imbalance. The results have shown that the network is learning useful things now: (results after 30 epochs)

Accuracy is approx. ~70% (down from ~97%);

Precision is approx. ~20% (up from 0%);

Recall is approx. ~60% (up from 0%).

These numbers were reached at around 20 epochs and didn't change significantly after that.

!! The results stated above are merely a proof of concept, they were obtained by training a simple network on a 10:1 imbalanced dataset. !!

1 Answer

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by (33.2k points)

You can use the Infogain Loss layer to compensate for the imbalance in your training set.

For example:

[cost of predicting 1 when gt is 0,    cost of predicting 0 when gt is 0

 cost of predicting 1 when gt is 1,    cost of predicting 0 when gt is 1]

It will help you to set the entries of H to reflect the difference between errors in predicting 0 or 1.

Hope this answer helps.

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