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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 RIPPER. 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?

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  • $\begingroup$ Why not use a decision tree model such as a random forest? $\endgroup$
    – Emre
    Nov 6 '16 at 22:00
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RIPPER (Repeated Incremental Pruning to Produce Error Reduction) is a method to automatically learn rule sets.

It has shown to perform better than other decision tree algorithms (e.g., C4.5). However, it performs work than Random Forest.

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