Imbalanced classification vs anomaly detection Currently I have a dataset with a class distribution of 75:25.
While the 1st thought that came to my mind was to use the binary classification using logistic regression. Of course, I can upsample using SMOTE etc to make reliable predictions for minority class.
However, I had another question.
Why can't this problem be formulated as anomaly detection? Meaning, since I have too much of positives (to learn from), is it okay to consider that anything that doesn't fall with the positive examples, can it be considered as negative? So, would anomaly detection work?
q1) Is there any disadvantages to this?
q2) can help me understand why do you think classification would be the best approach for imbalanced classification? If not, why do you think anomaly detection would be the best approach?
q3) when to choose binary clf or anomaly detection?

I will explain why anomaly detection should not be considered as classification problem by taking an example of anomaly detection using time series.
Let us say we have a time series that has some anomalies, and the anomaly points are present above and below the normal time series points. Now if we apply the clustering method(DBSCAN) to this time series, you will have a cluster something as shown below.

Had we considered this problem as a binary classification problem, we would get two clusters, here even though I applied the clusters (k=1) to the algorithm, you can see the anomalous points are no where seen to be forming cluster, they are scattered,  this does not make sense to consider as another cluster.
