Why do we need samples of both classes for the training of binary classification algorithms, if one-class algorithms can do the job with only samples from one class?
I know that one-class algorithms (like one-class svm) were proposed with the absence of negative data in mind and that they seek to find decision boundaries that separate positive samples (A) from negative ones (Not A).
Hence the traditional binary classification problem (between (A) and (B) for example) can be formulated as a classification of (A) and (not A = B). Is it about better classification results or am I missing something? Thank you in advance