A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph

Table 3

Precision.

Dataset	SVM	CS-EN-HN	SMOTE + C4.5	SMOTE-RSB* + C4.5	NRSBoundary-SMOTE + C4.5	N-HyperGraph
Bupa	0.8571	0.6827	0.5663	0.6581	0.5614	0.5497
Colic	0.6857	0.5887	0.7391	0.8103	0.7687	0.9151
Reprocessed	0.0000	0.6344	0.7030	0.7187	0.6979	0.5558
Machine	0.0000	0.7542	0.8250	0.8873	0.9041	0.9025
Labor	0.9411	1.0000	0.6667	0.6667	0.8421	0.7733
Tic	0.9908	0.6534	0.6882	0.8044	0.8100	0.9942
Iris	0.9245	0.5061	0.9057	0.8703	0.8888	0.8310
Seed	0.9295	0.9750	0.9577	0.9577	0.9577	0.8614
Vc	0.0000	0.6133	0.6696	0.7529	0.6695	0.4842
Glass	0.8775	1.0000	0.6933	0.8214	0.7428	0.8652
Haberman	0.4999	0.2334	0.4516	0.4590	0.4948	0.7433
Transfusion	0.4186	0.3139	0.4722	0.5299	0.5000	0.7116
Abalone (7 : 15)	0.7951	0.7552	0.8056	0.8155	0.8100	0.9413
Balance-scale	0.0000	0.1605	0.0000	0.0000	0.0000	0.4942
Abalone (9 : 18)	0.0000	0.3910	0.4167	0.4347	0.5000	0.9500
Yeast (POX : CTY)	1.0000	0.6371	0.6000	0.9268	0.7736	0.7000
Car	0.5000	0.5100	0.6849	0.6849	0.6857	0.6731
Yeast (ME2 : others)	0.0000	0.1272	0.3214	0.4706	0.4200	0.3265
Average	0.5233	0.5853	0.6204	0.6816	0.6682	0.7374

= 0.001 to 0.6.