I have built a CNN model to classify positive and negative in my data, the accuracy is around 85% with FPR is 16%. I know the FPR is high but it gives an acceptable number of FP in training and testing of balanced dataset and I was happy with that. However, after that, I was given an extremely high imbalanced dataset to predict, where it has a number of negative 770 times higher than positive, therefore, the FP after predicting is extremely high compared to TP. Is there any way that I can do to reduce that FPR? I have thought of making another model to re-classify (TP and FP) in hope to rescure more FP.