I am using a CNN to predict large changes in my target variable X. I am classifying several "set-up" states visually from my images.
I am only interested in big changes, or maybe no change. So, I can classify in 3 ways:
x is future period absolute change in X
- Label BigChange when x > A%. Label Flat when x < A%.
- Label BigChange when x > A%. Label Flat when x < B%, where B < A.
- Label BigChange when x > A%. Label Flat when x < B%. Label SmallChange when x > B && x < A.
Which of these approaches is likely to give me the best predictive accuracy for BigChange?
EDIT 1: Additional clarification
Input data is a set of images (60x60) with connection to Target variable (Float) X.
x is the delta between X values, and a big value might be 2-3% change. I am interested when the next x > some A (maybe 2%) i.e. there is a BigChange, but if x < B ( maybe 0.25% ) then I call that Flat. I want to classify my images so that I can predict a big change in x or when change is small. But I am NOT interested in predicting anything else (like changes > 0.25% but < 2%).
So, I am asking whether I should just classify and train on images that I have previously labelled BigChange or Flat. BUT this means when I run model on new data it will received lots of images that represent changes in x that are NOT Big or Flat. Is this problematic? Should I label things I am not interested in aswell for completeness?