I am developing a medical algorithm working on large amounts of data. However, as in many medical scenarios I have a lot of negative example and only a few positive ones. So any neural network I train is clearly biased towards negative classification. In fact it misclassifies most positive examples and although overall accuracy is OK, false negative rate is sky high.
I wanted to know what is the best way to make the network achieve lower false negative rate, I thought about duplicating the positive examples several times or maybe reducing the negative examples randomly, What do you think?