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I am facing a two-class classification problem where:

  1. There is way more training data in class 1 than in class 0.

  2. Classifying a class 0 event as class 1 has a higher loss than classifying a class 1 event as class 0.

  3. The prior probability of an event belong to class 0 is about 50 times smaller than the probability of belonging to class 1.


The way I see it I have (at least) 4 mechanisms to address these issues:

a. Collecting more data for class 0 than class 1 makes the classifier biased towards class 0.

b. Weighting the samples in class 0 higher than the ones in class 1.

c. Introducing an asymmetric loss function (which I understand as being identical to a 0-1 loss function in the weighted class case)

d. Given that the classifier gives returns a probability with which it assumes that a sample belongs to class 0, use a threshold smaller than 0.5 to assign a sample to class 0.

To me it seems like all four mechanisms are capable of addressing issues 1) and 3) (but not 2)) so I am wondering what is the cleanest way of addressing this?

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