I have a set of 400 labeled samples (8 numeric features) on which I trained a binary classifier.
The problem I am facing is that once the classifier is shipped to the users, I will get additional samples, but those will be unlabeled. I was researching common ways to incorporate unlabeled data in order to increase future classification accuracy. The way I see it I have 4 options:
Forget about the existing binary classifier and use a semi-supervised learning algorithm such as S3VM
Keep the existing binary classifier, use a transductive learning algorithm, such as label propagation, and use the newly (but possibly wrongly) labeled data to retrain the binary classifier; iterate this procedure.
Keep the existing binary classifier, use a (supervised?) clustering algorithm to label new data, and use the newly (but possibly wrongly) labeled data to retrain the binary classifier; iterate this procedure. Maybe some mixture model with Expectation Maximization?
While 3) seems rather flawed, because usual clustering algorithm optimize criterions different from labels I am not sure what to think about 1) and 2). What I do not like in 2) is that after we use a label propagation algorithm, we assume that these labels are correct and based on this new set of samples, we want to select new features and retrain our classifier. But a change in the missclassification rate now can stem form either a bad selection of features but might as well stem from the fact that the new labels are wrong. To me, 1) seems to reflect the situation the best. Am I understanding the situation correctly, i.e., is it true that 1) is superior to 2) and 2) is superior to 3)?
Or did I miss the point completely and an alternative approach is more appropriate than any of the 3?