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Table 15 Best learning schemes models performance (Continued)

From: A genetic algorithm based framework for software effort prediction

DS C LS S MMRE MdMRE MMAR MdMAR MSA MdSA Pred25
3 4 2X5X1 0.80 0.66 0.48 2,680 876 58.16 72.81 0.43
3 5 7X4X6 0.79 0.55 0.55 2,536 1,021 60.42 72.88 0.39
3 5 7X5X6 0.78 0.56 0.56 2,559 1,027 60.06 72.74 0.38
3 6 2X1X6 0.80 0.94 0.55 2,872 1,017 55.17 70.14 0.38
3 6 2X4X6 0.81 0.94 0.55 2,862 1,040 55.33 70.22 0.38
3 6 2X5X6 0.81 0.93 0.54 2,845 990 55.60 70.88 0.39
3 6 7X2X6 0.78 0.57 0.57 2,586 1,040 59.64 72.03 0.37
3 7 2X1X1 0.78 0.66 0.50 2,716 921 57.60 72.27 0.42
3 7 2X5X1 0.80 0.66 0.48 2,680 876 58.16 72.81 0.43
3 8 1X4X6 0.79 0.55 0.55 2,547 1,023 60.25 72.77 0.39
3 8 1X5X6 0.78 0.57 0.56 2,571 1,050 59.86 72.38 0.38
3 8 2X4X6 0.81 0.94 0.55 2,864 1,029 55.29 70.57 0.39
3 9 2X4X1 0.80 0.66 0.48 2,686 879 58.07 72.75 0.43
3 9 2X5X1 0.79 0.66 0.49 2,691 897 58.00 72.58 0.43
4 1 6X5X3 0.74 0.80 0.65 2,815 1,236 56.06 65.63 0.34
4 2 6X5X3 0.74 0.79 0.68 2,801 1,251 56.28 66.05 0.34
4 2 6X1X3 0.74 0.80 0.65 2,799 1,234 56.32 66.04 0.35
4 3 7X1X6 0.77 0.60 0.56 2,596 988 59.47 71.15 0.39
4 3 7X5X6 0.76 0.60 0.58 2,624 1,026 59.04 70.50 0.38
4 4 6X1X3 0.74 0.79 0.68 2,801 1,251 56.28 66.05 0.34
4 4 6X5X3 0.74 0.80 0.65 2,799 1,234 56.32 66.04 0.35
4 5 2X2X1 0.76 0.56 0.54 2,768 984 56.79 69.85 0.41
4 6 5X2X1 0.71 0.68 0.72 3,143 1,304 50.94 62.65 0.31
3 4 2X1X1 0.78 0.66 0.50 2,716 921 57.60 72.27 0.42