Cross posted from data science due to lack of response
This is probably a thing I am just not searching for correctly, but essentially my idea is this: given some machine learning classification $C$ based on an input dataset $D$, certain observations in $D$ are more likely to be misclassified than others because they are "less common". So what we would want to do is oversample observations like that, until $C$ is trained such that all observations have the probability of being classified correctly.
Is there a resampling method that does such a thing? My idea would be to
- train classifier $C$ on base dataset $D$
- take all observations misclassified by $C$ in $D$ and insert into new dataset $D'$
- retrain $C$ on $D = D + D'$ (or add a new batch with $D'$ in SGD etc)
- iterate that way until some convergence happens
Has someone formalized something along these lines? Intuitively, we want to overweight under-represented types of data.
Update: An additional thought I had that something along these lines might be useful in Q-learning - that instead of just randomly exploring off-policy, explore in areas where the model is less confident.