Ranking

Based on a thorough evaluation using 104 imbalanced datasets, the following 10 techniques provide the highest performance in terms of the AUC, GAcc, F1 and P20 scores, in nearest neighbors, support vector machine, decision tree and multilayer perceptron based classification scenarios. For more details on the evaluation methodology, see our paper on the comparative study.

sampler	overall	auc	rank_auc	gacc	rank_gacc	f1	rank_f1	p_top20	rank_ptop20
polynom-fit-SMOTE	2.5	0.902538	6	0.870753	1	0.695154	1	0.992496	2
ProWSyn	4.5	0.904389	1	0.868449	4	0.690284	3	0.991112	10
SMOTE-IPF	7.5	0.902565	5	0.868715	3	0.687935	9	0.990904	13
Lee	8	0.902318	7	0.868324	5	0.688082	8	0.991008	12
SMOBD	9.25	0.902247	8	0.86766	6	0.688885	4	0.990583	19
G-SMOTE	13.5	0.901916	10	0.865103	18	0.686613	12	0.990846	14
CCR	14.25	0.902112	9	0.861994	30	0.687886	10	0.991254	8
LVQ-SMOTE	14.75	0.902799	3	0.862295	29	0.683646	24	0.992211	3
Assembled-SMOTE	15.5	0.902691	4	0.866914	7	0.688614	5	0.982685	46
SMOTE-TomekLinks	15.75	0.901016	14	0.866174	9	0.684708	20	0.990573	20