How to perform binary classification when one class is more predominant than the other? Assuming we have big $m \times n$ input dataset, with $m \times 1$ output vector. It's a classification problem with only two possible values: either $1$ or $0$.
Now, the problem is that almost all elements of the output vector are $0$s with a very few $1$s (i.e. it's a sparse vector), such that if the neural network would &quot;learn&quot; to give always 0 as output, this would produce high accuracy, while I'm also interested in learning when the 1s occurs.
I thought one possible approach could be to write a custom loss function giving more weight to the 1s, but I'm not completely sure if this would be a good solution.
What kind of strategy can be applied to detect such outliers?
As described in this post, this problem is known as &quot;unbalanced dataset&quot; problem, which can have different solution approaches. If you use supervised learning, augmentation approaches
could  help. Otherwise, unsupervised approaches need some proper distance measure for outliers detection.