I have a dataset belonging to three different classes: A, B and C. Among these three classes, the classification for label C is unreliable comparing to other two classes. In other words, some of the samples in class C is actually belong to class A and class B. For now, I need to run some supervised learning (logistic regression, decision tree and random forests) models. According to the confusion matrix, the classification between A and B is relatively accurate, but the classification between C and other two classes is not acceptable. I wondered whether is any way to deal with this issue?
For now, I'm considering to use a clustering algorithm for the samples in class C before runnning the model. After dividing the samples in class C into 3 groups, try to find a relatively better group as the datasets for class C.