I have hand writed classifiers (there are a lot of them). It's implemented as collection of rule sets
IIF - THEN.
I want to optimize the % of errors. There some classifiers witch have vey big % of
False Positive and
False Negative results.
During my reserch about this problem i've found
RIPPER alghorytm witch, seems like, was designed to solve this kind of problems.
Also there are
Multi Naive Bias alghorythm that can be helpfull.
As far as i understand, usualy in
EA there is
Global Optimization step, withc usually/sometimes implemented via
So, basicly. i have manually generated
rule-set witch i have optimize now, with
RIPPER for example.
Is it true? Can You recomend some literature?