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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