Skip to main content

Table 9 Analysis of LVH versus OVA

From: Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Data set

Over_sampling techniques

B

C

D

E

F

G

H

I

J

K

%RDD

%RDA

%RDD

%RDA

%RDD

%RDA

%RDD

%RDA

%RDD

%RDA

%RDD

%RDA

%RDD

%RDA

%RDD

%RDA

%RDD

%RDA

%RDD

%RDA

Yeast

84.4

1.34

87.69

1.3

85.27

1.32

87.51

2.41

88.22

1.34

86.03

2.38

51.47

2.35

23.09

2.32

87.19

1.16

83.44

2.56

Car

11.76

2.12

9.12

1.05

5.91

0

10.37

2.11

5.91

0

9.91

1.04

1.79

1.03

9.59

1.03

11.34

0

6.16

2.12

KEGG-U

4.97

2.19

3.81

2.17

3.28

3.27

4.15

2.15

13.01

3.31

6.31

1.07

3.89

1.06

4.85

1.04

5.38

1.05

6.57

1.08

Average

34.73

1.88

33.54

1.13

31.48

1.53

34.01

2.22

35.71

1.55

34.08

1.49

19.05

1.11

12.51

1.1

34.64

0.55

32.6

1.44

  1. %RDD: % relative difference in data set instances using OVA over LVH method in comparison to initial data set
  2. %RDA: % relative difference in AUC values (Random Forest–classifier) of OVA over LVH method in comparison to base AUC values