Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Table 2 Description of 20 imbalanced datasets

Dataset	n	\(n_{{\text {min}}}\)	\(k_{{\text {min}}}\)	d	\(d^u\)	\(d^o\)	c
Abalone19	4174	32	0.008	8	7	1	2
Abalone9-18	731	42	0.057	8	7	1	2
Ecoli-0_vs_1	220	77	0.350	7	5	2	2
Ecoli-0-1-3-7_vs_2-6	281	7	0.025	7	5	2	2
Ecoli1	336	77	0.229	7	5	2	2
Ecoli2	336	52	0.155	7	5	2	2
Ecoli3	336	35	0.104	7	5	2	2
Ecoli4	336	20	0.060	7	5	2	2
Glass0	214	70	0.327	9	9	0	2
Glass1	214	76	0.355	9	9	0	2
Glass2	214	17	0.079	9	9	0	2
Glass4	214	13	0.061	9	9	0	2
Glass5	214	9	0.042	9	9	0	2
Page-blocks0	5472	559	0.102	10	10	0	2
Pima	768	268	0.349	8	8	0	2
Segment0	2308	329	0.143	19	18	1	2
Vehicle0	846	199	0.235	18	18	0	2
Vowel0	988	90	0.091	13	11	2	2
Wisconsin	683	239	0.350	9	9	0	2
Yeast-0-5-6-7-9_vs_4	528	51	0.097	8	7	1	2

n, number of instances; \(n_{min}\), number of minority classes; \(k_{min}\) minority class ratio; \(d\) number of attributes; \(d^u,\) number of numeric attributes; \(d^o,\) number of nominal attributes; c, number of classes