oversampling some classes from time series data

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For some classification needs. I have multivariate time series data composed from 4 stelite images in form of (145521 pixels, 4 dates, 2 bands) I made a classification with tempCNN to classify the data into 5 classes. However there is a big gap between the class 1,2 with 500 samples and 4,5 with 1452485 samples.

I' am wondering if there is a method that help me oversamling the two first classes to make my dataset more adequate for classification.

oversampling classification python
1 Answers

actually there is a lib in python for that "imbalanced-learn" (although u can do it manually) .

you can check the docs it's very easy to use

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