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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.