# Classification: keeping false positive in training set

I am working on a classifier, with a large number of possibles classes, and also a no class class.
My training set is made of the output of a hardcoded logic that is currently made for classification. Obviously, this hardcoded method is not perfect, hence the classifier I am working on now.
The output of the hard coded method has true and false output. My first thought was to only keep the true output (when the output was right), but I am wondering if there is a way to also keep the false output, so the model can train on it, knowing that it is not the right output.
If a class is ABC, I can create 2 classes ABC_true, and ABC_false, but how would the model know that we are talking about the same class, one for its true output, one for its false output?

Would encoding each possible class as dummies features for the model be a solution, and setting the dummy to one for each class that I am sure it is not the right outcome?

Thank you

• If the classifier is hardcoded logic, how are you doing any training at all? It's also not clear by what you mean by "keep" the true/false output - the training set is what it is, regardless of how you classify it. Are you talking about some kind of boosting algorithm, where you iteratively reweight training examples so that misclassified samples are given more weight? – Nuclear Wang Feb 22 '19 at 16:06
• Thanks for your comment. I want to get rid of the hardcoded classifier, so I am developing a ML one. But I am going to use the output of the currently hardcoded classifier as a training set for the ML one. – antoine.trdc Feb 27 '19 at 17:27