Table 6.

Evaluation results of Breast dataset using different sampling strategies with three metrics across ten classification algorithms.

Method	Metric	Classifier
		J48	3NN	NB	RF5	LOG	1NN	7NN	SMO	RF10	RBFNet	R. Avg.
PSO	AUC	0.580	0.610	0.602	0.603	0.736	0.570	0.625	0.670	0.637	0.549	0.618
	FMeasure	0.418	0.423	0.393	0.369	0.489	0.394	0.425	0.487	0.392	0.359	0.415
	GMean	0.593	0.599	0.577	0.550	0.661	0.553	0.595	0.660	0.577	0.544	0.591
	C. Avg.	0.530	0.544	0.524	0.507	0.629	0.506	0.548	0.606	0.535	0.484	0.541
RU	AUC	0.562	0.597	0.587	0.604	0.722	0.515	0.612	0.650	0.639	0.568	0.606
	FMeasure	0.393	0.407	0.378	0.403	0.479	0.345	0.422	0.466	0.399	0.388	0.408
	GMean	0.552	0.583	0.558	0.571	0.656	0.502	0.597	0.637	0.581	0.568	0.581
	C. Avg.	0.502	0.529	0.508	0.526	0.619	0.454	0.544	0.584	0.540	0.508	0.532
RO	AUC	0.569	0.564	0.596	0.593	0.789	0.544	0.582	0.688	0.639	0.480	0.604
	FMeasure	0.327	0.391	0.384	0.354	0.560	0.325	0.382	0.509	0.297	0.286	0.382
	GMean	0.508	0.565	0.569	0.522	0.701	0.512	0.560	0.683	0.452	0.475	0.555
	C. Avg.	0.468	0.507	0.516	0.490	0.683	0.460	0.508	0.627	0.463	0.414	0.514
Cluster	AUC	0.543	0.616	0.538	0.554	0.711	0.547	0.603	0.641	0.586	0.528	0.587
	FMeasure	0.384	0.402	0.356	0.325	0.466	0.371	0.417	0.450	0.342	0.332	0.385
	GMean	0.579	0.585	0.550	0.508	0.648	0.527	0.583	0.638	0.540	0.529	0.569
	C. Avg.	0.502	0.534	0.481	0.462	0.608	0.482	0.534	0.576	0.489	0.463	0.514