The goal is to classify three different cell types based on certain features (e.g. area, shape tensor etc.). However the amount of labelled training data I have is very small. Therefore, it was suggested to create a synthetic data set trying to mimic the real one which we could use to train the classifier. Hopefully, this would allow classification of the real cells.
The question is, how do I create proper synthetic data for this problem?
Someone suggested using averages of the limited data present and adding stochastic variations to the features. However, I don't think that I would really get new information based on this approach. Is this correct?
Therefore, my question is: Can I generate synthetic data based on the present real data or would I simply be adding more of the "same"? If this approach is worthless, are there suggestions for better ways to solve the classification problem?