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Table 3 Performance comparison among AdaBoost, Cost-sensitive decision tree, AdaCost, and our method RankCost with respect to F-measure (F), TPR (R), and PPV (P) values and and their 95 % confidence intervals

From: Learning to improve medical decision making from imbalanced data without a priori cost

   AdaBoost Cost-sensitive AdaCost RankCost
    Decision tree   
Breast Cancer Cost   1:0.2 1:0.1  
  F 0.465 [0.217,0.713] 0.468 [0.366,0.570] 0.502 [0.358,0.646] 0.494 [0.376, 0.612]
  R 0.388 [0.104, 0.672] 0.765 [0.423,1.107] 0.906 [0.603, 1.209] 0.494 [0.270, 0.718]
  P 0.579 [0.152, 1.000] 0.337 [0.263, 0.411] 0.347 [0.211, 0.483] 0.494 [0.392, 0.596]
Hepatitis Cost   1:0.4 1:0.1  
  F 0.5 [0.002,0.998] 0.603 [0.261, 0.945] 0.628 [0.200,1.056] 0.628 [0.280,0.976]
  R 0.469 [-.215, 1.153] 0.688 [0.212, 1.164] 0.719 [0.165,1.273] 0.843 [0.465,1.221]
  P 0.536 [-.122,1.194] 0.537 [0.047,1.022] 0.561 [0.159, 0.963] 0.500 [0.158,0.842]
Diabetes Cost   1:0.4 1:0.5  
  F 0.595 [0.447,0.743] 0.658 [0.472,0.844] 0.661 [0.493,0.829] 0.692 [0.532,0.852]
  R 0.526 [0.308,0.744] 0.757 [0.589,0.825] 0.731 [0.527,0.935] 0.802 [0.638,0.966]
  P 0.684 [0.508,0.860] 0.582 [0.400,0.764] 0.603 [0.403,0,803] 0.609 [0.431,0.787]
Sick Euthyroid Cost   1:0.2 1:0.2  
  F 0.007 [-.033,0.047] 0.645 [0.549,0.741] 0.616 [0.538,0.694] 0.612 [0.538,0.686]
  R 0.003 [-.017,0.023] 0.826 [0.714,0.938] 0.785 [0.659,0.911] 0.942 [0.814,1.070]
  P 0.1 [-.500,0.700] 0.53 [0.416,0.644] 0.507 [0.429,0.585] 0.453 [0.389,0.517]
  1. The cost settings are those with which AdaBoost and Cost-sensitive decision tree can achieve the best F-measure values.
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