4
votes
Accepted
How do I select the (number of) negative cases, if I'm given a set of positive cases?
Short answer
To select the proper dataset to construct, you should first figure out a metric to use to compare, and then select the dataset construction that gives the better metric. There is no ...
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3
votes
Accepted
Does data skew matter in classification problem?
Yes. Skewed data is one of the biggest problems in AI applications. As you rightly identified, the real world distribution is skewed. Doing a random sampling results in one major issue of an uneven ...
2
votes
Accepted
How robust are deep networks to class imbalance?
@nbro pointed out the paper A systematic study of the class imbalance problem in convolutional neural networks, which tested class imbalance LeNet for MNIST, on a custom CNN for CIFAR-10, and on ...
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2
votes
How to deal with datasets which are not balanced?
My favorite first alternative is change the error/cost function in order to penalize more the errors in the less frequent label.
About other alternatives (generate synthetic cases, ..) you can easily ...
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2
votes
Accepted
How to handle class imbalance when the actual data are that way
You can use stratified cross-validation combined with an imbalanced learning technique applied to the training data. Stratification ensures that when you split your data into train and test, the ratio ...
- 391
1
vote
How to deal with an unbalanced dataset?
4861/5111 is about 95.1%, so it looks like your classifier is probably predicting every patient as "no stroke" (i.e. it is not really doing anything useful). The thing to do is to work out ...
- 121
1
vote
What does "adding class weights for an imbalanced dataset" mean in the case of multi-label classification?
The paper A systematic study of the class imbalance problem
in convolutional neural networks is a great overview on class imbalance approaches. Section 2 summarizes various methods commonly used. They ...
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1
vote
Accepted
How to arrange test dataset distribution for an imbalanced classification problem?
The test set should represents the "real" data distribution your model will tackle once deployed and used in real applications. So the quick answer is yes, the test data should be imbalanced,...
- 3,503
1
vote
Accepted
How do you handle unbalanced image datasets?
You can always adjust class weights accordingly. I know the reference is not for image data but it shouldn't matter if you are doing classification. Here is another answer more direct to the point.
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1
vote
Accepted
How to handle an unbalanced dataset when training object detection algorithms?
One thing to try first is Focal Loss. This particular loss works well for classification or object detection where your dataset is unbalanced and contains many classes. In short, the loss suppresses ...
- 41
1
vote
Accepted
How can I use Generative Adversarial Networks to solve the imbalanced class problem?
In my experience, GANs work really well for the scenario of semi-supervised learning, where you don't necessarily have labels for all your class $B$ data, but you do have a balanced dataset. In my (...
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1
vote
Does data skew matter in classification problem?
Definitely. NNs can learn the data that you teach to them. If you teach them biased, the network will be biased. As you mention, one solution is to reduce the data that you have. However, it is not ...
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