CODEWITHSUNDEEP
machine-learningkerasclassification
Wolfgang Schneider
Wolfgang Schneider

Reputation: 1

Classification with Keras, unbalanced classes

I have a binary classification problem I'm trying to tackle in Keras. To start, I was following the usual MNIST example, using softmax as the activation function in my output layer.

However, in my problem, the 2 classes are highly unbalanced (1 appears ~10 times more often than the other). And what's even more critical, they are non-symmetrical in the way they may be mistaken.

Mistaking an A for a B is way less severe than mistaking a B for an A. Just like a caveman trying to classify animals into pets and predators: mistaking a pet for a predator is no big deal, but the other way round will be lethal.

So my question is: how would I model something like this with Keras?

thanks a lot

Upvotes: 0

Views: 243

Answers (1)

akilat90
akilat90

Reputation: 5696

A non-exhaustive list of things you could do:

  1. Generate a balanced data set using data augmentations. If the data are images, you can add image augmentations in a custom data generator that will output balanced amounts of data from each class per batch and save the results to a new data set. If the data are tabular, you can use a library like imbalanced-learn to perform over/under sampling.

  2. As @Daniel said you can use class_weights during training (in the fit method) in a way that mistakes on important class are penalized more. See this tutorial: Classification on imbalanced data. The same idea can be implemented with a custom loss function with/without class_weights during training.

Upvotes: 1

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