If I use model that provide output probability in imbalanced case (say ratio between majority and minority class is 100 : 1), I saw that the output probability of data points from majority class is very High (say 99% or so), and much higher than output probability of data points from minority class. The problem is: In case of abnormality detection in banking or in many cases in medical study, we just want to detect the minority class. So I want to increase the output probability of minority class. What can we do in this case? I searched many sources on the internet and papers, but did not see any solutions to this problem. Maybe because people in machine learning mostly care about some metrics like accuracy, then they just apply under/over sampling to improve performance.
Thank you for reading my question.