Table 6.

Comparison of the performance of the learning models in full training mode—Monk’s Problem 2 dataset.

Learning Model	Number of Rules	Percent Correct	TP Rate	FP Rate	Precision	Recall	F-Measure	MCC	ROC Area	PRC Area	RMSE
ENORA-ACC	7	75.87	0.759	0.370	0.753	0.759	0.745	0.436	0.695	0.680	0.491
ENORA-AUC	7	68.71	0.687	0.163	0.836	0.687	0.687	0.523	0.762	0.729	0.559
ENORA-RMSE	7	77.70	0.777	0.360	0.777	0.777	0.762	0.481	0.708	0.695	0.472
NSGA-II-ACC	7	68.38	0.684	0.588	0.704	0.684	0.597	0.203	0.548	0.580	0.562
NSGA-II-AUC	7	66.38	0.664	0.175	0.830	0.664	0.661	0.497	0.744	0.715	0.580
NSGA-II-RMSE	7	68.71	0.687	0.591	0.737	0.687	0.595	0.226	0.548	0.583	0.559
PART	47	94.01	0.940	0.087	0.940	0.940	0.940	0.866	0.980	0.979	0.218
JRip	1	65.72	0.657	0.657	0.432	0.657	0.521	0.000	0.500	0.549	0.475
OneR	1	65.72	0.657	0.657	0.432	0.657	0.521	0.000	0.500	0.549	0.585
ZeroR	-	65.72	0.657	0.657	0.432	0.657	0.521	0.000	0.500	0.549	0.475