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Table 2 Description of the experimental datasets

From: A multi-manifold learning based instance weighting and under-sampling for imbalanced data classification problems

Name

#Attributes

#Minority Class

#Majority Class

#Examples

Imbalance Ratio

ecoli1

7

77

259

336

3.36

ecoli2

7

52

284

336

5.46

ecoli3

7

35

301

336

8.60

ecoli4

7

20

316

336

15.8

ecoli0147vs56

6

25

307

332

12.28

ecoli034_5

7

20

180

200

9

ecoli0147_2356

7

29

307

336

10.59

glass0

9

70

144

214

2.06

glass0123456

9

51

163

214

3.20

kddcup- buffer_overflow_vs_back

41

30

2203

2233

73.43

kddcup-rootkit-imap_vs_back

41

22

2203

2225

100.14

kddcup-guess_passwd_vs_satan

41

53

1589

1642

29.98

kddcup-land_vs_portsweep

41

21

1040

1061

49.52

kddcup-land_vs_satan

41

21

1589

1610

75.67

new-thyroid1

5

35

180

215

5.14

page-blocks-1-3_vs_4

10

28

444

472

15.86

Pima

8

268

500

768

1.87

segment0

19

329

1979

2308

6.02

shuttle_2_vs_5

9

49

3267

3316

66.67

vehicle2–1

18

218

628

846

2.88

vowel0

13

90

898

988

9.98

Wisconsin

9

239

444

683

1.86