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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