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Table 15 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

1

1

2X3X8

0.70

1.11

0.89

3,379

1,665

47.26

59.70

0.28

1

1

6X3X8

0.71

1.09

0.86

3,348

1,628

47.73

60.24

0.28

1

2

1X1X11

0.75

0.55

0.63

2,809

1,063

56.15

67.83

0.32

1

2

1X4X11

0.74

0.55

0.63

2,835

1,072

55.74

67.21

0.32

1

3

1X5X6

0.74

0.57

0.58

2,810

1,027

56.13

69.47

0.38

1

3

6X1X3

0.74

1.65

1.24

4,217

2,325

34.17

45.10

0.17

1

3

6X1X6

0.73

0.66

0.59

2,946

1,079

54.01

68.70

0.39

1

3

7X4X6

0.73

0.57

0.58

2,807

1,036

56.18

69.99

0.38

1

3

7X5X6

0.74

0.57

0.57

2,797

994

56.34

70.44

0.38

1

4

1X1X11

0.75

0.55

0.63

2,809

1,063

56.15

67.83

0.32

1

4

1X4X11

0.74

0.55

0.63

2,835

1,072

55.74

67.21

0.32

1

5

1X1X11

0.73

0.57

0.64

2,880

1,078

55.05

66.68

0.31

1

5

1X4X11

0.74

0.56

0.63

2,845

1,088

55.59

67.11

0.31

1

5

1X4X3

0.74

1.07

1.02

3,735

1,860

41.69

53.87

0.19

1

5

1X5X11

0.75

0.55

0.63

2,810

1,056

56.14

67.92

0.32

1

5

7X4X3

0.74

1.08

1.02

3,747

1,863

41.51

53.54

0.19

1

5

7X5X11

0.74

0.55

0.63

2,842

1,073

55.64

67.50

0.31

1

6

6X2X4

0.74

1.13

1.03

3,899

1,889

39.14

53.94

0.26

1

6

6X3X4

0.72

1.23

1.13

4,010

2,036

37.41

51.15

0.25

1

6

7X5X11

0.73

0.61

0.65

2,917

1,166

54.46

65.77

0.31

1

7

1X1X11

0.75

0.55

0.63

2,809

1,063

56.15

67.83

0.32

1

7

1X4X11

0.74

0.55

0.63

2,835

1,072

55.74

67.21

0.32

1

8

6X2X4

0.74

1.13

1.03

3,899

1,889

39.14

53.94

0.26

1

8

6X3X4

0.72

1.23

1.13

4,010

2,036

37.41

51.15

0.25

1

8

7X5X11

0.73

0.61

0.65

2,917

1,166

54.46

65.77

0.31

1

9

1X1X2

0.74

0.56

0.59

2,856

1,029

55.42

69.80

0.34

1

9

1X2X2

0.74

0.56

0.59

2,866

1,051

55.26

68.64

0.33

1

9

1X2X6

0.74

0.55

0.58

2,711

1,018

57.68

69.66

0.38

1

9

1X4X2

0.74

0.57

0.59

2,876

1,050

55.10

68.69

0.33

1

9

6X2X6

0.74

0.64

0.60

2,872

1,113

55.16

68.69

0.38

1

9

7X4X2

0.73

0.56

0.60

2,877

1,055

55.10

68.96

0.33

2

1

1X2X8

0.69

1.28

0.93

3,648

1,781

43.05

56.92

0.27

2

2

7X3X14

0.72

0.70

0.63

3,270

1,113

48.96

63.97

0.35

2

3

6X2X14

0.72

0.79

0.72

3,439

1,340

46.31

58.28

0.30

2

3

6X3X14

0.69

0.81

0.74

3,511

1,401

45.19

56.85

0.29

2

3

6X4X14

0.71

0.81

0.74

3,477

1,377

45.73

58.00

0.30

2

4

7X3X14

0.72

0.70

0.63

3,270

1,113

48.96

63.97

0.35

2

5

7X1X14

0.72

0.70

0.63

3,257

1,104

49.16

63.75

0.34

2

5

7X3X14

0.70

0.71

0.64

3,289

1,152

48.65

63.04

0.34

2

6

7X1X14

0.72

0.70

0.63

3,257

1,104

49.16

63.75

0.34

2

6

7X3X14

0.70

0.71

0.64

3,289

1,152

48.65

63.04

0.34

2

7

7X3X14

0.72

0.70

0.63

3,270

1,113

48.96

63.97

0.35

2

8

7X3X11

0.68

0.70

0.63

3,313

1,126

48.28

64.21

0.35

2

9

1X2X14

0.69

0.73

0.65

3,247

1,158

49.32

63.16

0.34

2

9

1X3X14

0.70

0.73

0.65

3,232

1,132

49.55

63.83

0.34

2

9

1X4X14

0.69

0.74

0.66

3,253

1,133

49.23

63.01

0.34

2

9

1X5X14

0.70

0.71

0.63

3,192

1,082

50.17

64.63

0.35

3

1

2X4X6

0.81

0.93

0.54

2,839

1,004

55.69

70.83

0.39

3

2

2X1X1

0.78

0.66

0.50

2,716

921

57.60

72.27

0.42

3

2

2X5X2

0.80

0.66

0.48

2,680

876

58.16

72.81

0.43

3

3

2X5X1

0.80

0.66

0.48

2,674

885

58.26

73.45

0.43