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Table 16 Best learning schemes models performance

From: A genetic algorithm based framework for software effort prediction

DS

C

LS

S

MMRE

MdMRE

MMAR

MdMAR

MSA

MdSA

Pred25

4

6

5X3X1

0.70

0.69

0.73

3,163

1,337

50.62

61.96

0.30

4

6

5X4X1

0.70

0.69

0.72

3,164

1,333

50.61

62.62

0.30

4

6

5X5X1

0.70

0.69

0.74

3,182

1,344

50.32

61.89

0.30

4

7

6X1X3

0.74

0.79

0.68

2,801

1,251

56.28

66.05

0.34

4

7

6X5X3

0.74

0.80

0.65

2,799

1,234

56.32

66.04

0.35

4

8

2X5X2

0.74

0.65

0.77

3,532

1,324

44.86

60.85

0.23

4

8

6X1X3

0.74

0.80

0.65

2,807

1,216

56.18

66.04

0.35

4

9

7X5X6

0.76

0.59

0.57

2,608

1,018

59.29

70.95

0.38

5

1

7X1X3

0.72

0.95

1.13

3,083

1,400

41.67

68.85

0.23

5

2

6X5X1

0.72

0.81

0.91

2,859

1,210

45.90

72.33

0.26

5

3

7X1X2

0.72

0.64

0.82

2,660

944

49.66

78.67

0.29

5

3

7X1X3

0.72

0.96

1.14

3,119

1,438

40.98

68.13

0.22

5

3

7X2X3

0.72

0.96

1.15

3,142

1,442

40.54

67.72

0.22

5

4

6X5X1

0.72

0.81

0.91

2,859

1,210

45.90

72.33

0.26

5

5

1X1X4

0.72

1.43

1.64

3,606

2,134

31.77

54.83

0.19

5

5

1X1X6

0.72

0.63

0.81

2,466

969

53.34

77.96

0.30

5

5

1X2X2

0.72

0.63

0.82

2,509

925

52.52

79.04

0.29

5

5

1X3X2

0.72

0.64

0.82

2,510

924

52.51

79.10

0.29

5

5

1X3X4

0.72

1.45

1.63

3,587

2,136

32.13

54.96

0.19

5

6

1X1X2

0.72

0.64

0.81

2,559

945

51.58

78.76

0.29

5

6

1X1X3

0.72

0.91

1.07

2,950

1,356

44.17

69.56

0.23

5

6

1X5X3

0.72

0.91

1.08

2,965

1,374

43.89

69.11

0.22

5

7

6X5X1

0.72

0.81

0.91

2,859

1,210

45.90

72.33

0.26

5

8

7X2X2

0.72

0.63

0.81

2,520

923

52.31

79.11

0.30

5

8

7X2X3

0.72

0.93

1.09

2,926

1,356

44.64

69.76

0.24

5

8

7X3X2

0.72

0.63

0.80

2,520

926

52.33

79.19

0.30

5

8

7X3X3

0.72

0.93

1.10

2,934

1,367

44.48

69.48

0.24

5

9

1X1X6

0.73

0.63

0.79

2,517

948

52.38

78.29

0.31

5

9

1X3X6

0.72

0.64

0.80

2,520

959

52.32

77.83

0.31

6

1

6X3X1

0.70

0.80

0.90

2,677

1,105

49.36

74.96

0.27

6

2

6X4X1

0.71

0.80

0.89

2,688

1,108

49.14

74.84

0.27

6

3

6X1X1

0.71

0.80

0.89

2,684

1,109

49.21

74.96

0.28

6

3

6X2X1

0.70

0.80

0.90

2,696

1,138

48.99

74.37

0.27

6

4

6X4X1

0.71

0.80

0.89

2,688

1,108

49.14

74.84

0.27

6

5

6X2X1

0.71

0.80

0.90

2,736

1,124

48.24

74.65

0.27

6

6

6X3X1

0.70

0.80

0.91

2,733

1,130

48.28

74.49

0.27

6

6

6X4X1

0.70

0.80

0.91

2,734

1,129

48.27

74.54

0.27

6

6

6X5X1

0.71

0.80

0.90

2,718

1,115

48.57

74.75

0.28

6

7

6X4X1

0.71

0.80

0.89

2,688

1,108

49.14

74.84

0.27

6

8

6X4X2

0.70

0.63

0.83

2,748

964

48.00

78.53

0.29

6

8

6X4X3

0.71

1.34

1.48

3,555

2,097

32.74

53.28

0.19

6

9

1X1X6

0.71

0.67

0.79

2,613

973

50.55

77.41

0.31

6

9

1X2X2

0.68

0.67

0.83

2,635

963

50.14

78.00

0.28

6

9

1X2X6

0.71

0.67

0.78

2,605

953

50.72

77.82

0.31

6

9

1X3X6

0.70

0.67

0.79

2,620

970

50.42

77.53

0.30

7

1

7X3X14

0.75

0.72

0.84

2,611

1,028

50.59

76.67

0.30

7

1

7X5X14

0.76

0.71

0.83

2,615

1,018

50.51

76.84

0.30

7

2

6X4X6

0.79

0.61

0.72

2,360

894

55.34

80.20

0.35

7

3

6X2X2

0.79

0.55

0.73

2,295

870

56.58

80.48

0.32

7

3

6X2X6

0.80

0.60

0.70

2,174

881

58.86

80.41

0.35

7

3

6X3X6

0.79

0.61

0.72

2,214

904

58.11

80.03

0.35