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Table 16 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
7 3 6X5X6 0.79 0.61 0.72 2,206 902 58.25 80.14 0.35
7 4 6X4X6 0.79 0.61 0.72 2,360 894 55.34 80.20 0.35
7 5 6X2X3 0.77 0.84 0.92 2,612 1,238 50.58 72.58 0.28
7 5 6X3X3 0.77 0.86 0.94 2,625 1,259 50.33 72.15 0.27
7 6 6X2X3 0.77 0.84 0.92 2,612 1,238 50.58 72.58 0.28
7 6 6X3X3 0.77 0.86 0.94 2,625 1,259 50.33 72.15 0.27
7 7 6X4X6 0.79 0.61 0.72 2,360 894 55.34 80.20 0.35
7 8 1X3X14 0.77 0.71 0.84 2,655 1,047 49.76 76.19 0.31
7 8 6X2X14 0.78 0.73 0.87 2,674 1,110 49.40 75.31 0.29
7 9 1X3X14 0.77 0.71 0.84 2,655 1,047 49.76 76.19 0.31
7 9 6X2X14 0.78 0.73 0.87 2,674 1,110 49.40 75.31 0.29
8 1 2X3X1 0.73 0.76 0.87 2,691 1,117 49.08 74.22 0.28
8 2 1X5X3 0.69 0.94 1.01 2,941 1,406 44.34 69.05 0.25
8 3 2X4X1 0.73 0.74 0.85 2,563 1,084 51.50 75.00 0.29
8 3 2X5X1 0.73 0.73 0.85 2,549 1,075 51.76 75.35 0.29
8 4 1X5X3 0.69 0.94 1.01 2,941 1,406 44.34 69.05 0.25
8 5 2X1X1 0.74 0.73 0.85 2,558 1,084 51.60 75.20 0.29
8 5 2X5X1 0.73 0.74 0.87 2,570 1,112 51.37 74.66 0.29
8 6 2X4X1 0.73 0.73 0.85 2,577 1,100 51.23 74.76 0.29
8 6 2X5X1 0.73 0.73 0.86 2,591 1,115 50.98 74.41 0.29
8 7 1X5X3 0.69 0.94 1.01 2,941 1,406 44.34 69.05 0.25
8 8 2X2X6 0.75 0.81 0.89 2,547 1,144 51.80 74.77 0.30
8 9 2X3X1 0.73 0.74 0.87 2,643 1,091 50.00 74.58 0.28
8 9 2X5X1 0.74 0.74 0.86 2,631 1,085 50.21 74.91 0.29