ROCCER: A ROC convex hull rule learning algorithm
نویسندگان
چکیده
In this paper we propose a method to construct rule sets that have a convex hull in ROC space. We introduce a rule selection algorithm called ROCCER, which operates by selecting rules from a larger set of rules in order to optimise Area Under the ROC Curve (AUC). Compared with set covering algorithms, our method is less dependent on the previously induced rules. Experimental results on three UCI datasets show significant improvements on two of these.
منابع مشابه
ROCCER: An Algorithm for Rule Learning Based on ROC Analysis
We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules – for instance found by Apriori – using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose th...
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