I am training a model, where class distribution is unbalanced. So, I thought of applying augmentation technique, to improve the performance. I am wondering,is it fair to apply augmentation only on class with low samples, or Do I apply augmentation on all data?
If we apply augmentation on only one class, will it create bias while training?
Please provide your valuable insights on this.
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i would not do that : imagine you have images and augment the minority class with some kind of blurring filter ; unless you are sure that similar blurring "naturally" occurs in your majority class, your new training set will be close to useless (blurred image => minority class, whatever the image content)
the best way to be sure that "similar blurring occurs in the majority class" is to not rely on this happening "naturally" (unless you have very strong prior knowledge about that), but to apply the blurring filter to the majority class too
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Its a really common problem in the natural data set. If you want to have balance results. You can under-sample or oversample the train data. Under sample is you take the lowest class and reduce the other class to that number. Oversample is the opposite of taking the highest and create a copy of the other class. Sometimes noise or other thing is added to the signal to give model little bit different input of the same data. In the case of image rotation, blurring changing the contrast could be done. Do not do it for testing because that will change the natural distribution of the data. Another way around it is by using the F1 score or weighted F1 score for the deciding factor of the model.
It really depends on the nature of your data and the problem you are precisely working on. However, in general, if your training samples are majorly containing samples from only 1 class, then the model would not really generalise, but would memorise. There is a danger of the model to introduce extreme bias towards the class with majority samples. What I would do in this scenario is to oversample the minority data points, holding out a specific unchanged portion for testing. Thus, the new training samples would have a balance in the 2 classes, and at the same time, you wouldn't risk making predictions on oversampled data, but on the data which you already separated for testing purposes before the oversampling. This may not be the best way, but is definitely one way we could try approaching the situation. Hope this helps.
Hi Friend, it depends on the nature of the problem you are solving and the percentage distribution of minority and majority class. For instance, I am currently undergoing a similar research with imbalance data set with distribution ratio of majority and minority class 99:1 respectively. In this case, I only applied SMOTE to the minority class because training with the raw data may introduce bias into the result.
Hi, the reason of imbalanced learning is about not only sample size but also learning ability. Please read the following paper:
AdaBalGAN: An Improved Generative Adversarial Network With Imbalanced Learning for Wafer Defective Pattern Recognition
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