Naive Bayes classification threshold adjustment

[Matti Lyra]

I've been involved in a machine learning project recently and am now
in the process of writing the project up for a paper submission. We
used the naive bayes classifier on the project and developed a method
for adjusting the classification of datapoints depending on how
exactly we want the classifier to perform, ie. increase or decrease
recall on one class in a binary classification problem. This is
similar to adjusting the classification threshold but is sensitive the
feature counts unlike just adjusting the classification threshold.

My question is if anyone knows of any previous research on methods
relating to adjusting the decision boundary in any other way than just
changing the classification threshold?

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