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Table 8 Comparison of methods by maximum Az value using 22 features (Calcification)

From: AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM

 

T

MGH

WU

WFUSM

SHH

SVM

 

0.77497

0.91710

0.89738

0.94945

SVM-RFE

 

0.77497

0.93436

0.89859

0.95332

ENSEMBLE

 

0.68951

0.76647

0.72650

0.85677

JOIN (1)

 

0.75259

0.92326

0.81433

0.91352

JOIN (2)

 

0.72296

0.82307

0.72987

0.80400

JOIN (3)

 

0.70815

0.76647

0.70059

0.67598

JOIN (4)

 

0.58656

0.69779

0.65667

0.55964

JOIN (5)

 

0.53520

0.63858

0.65667

0.51203

MSVM-RFE (bootstrap)

5

0.77497

0.91710

0.89988

0.95379

 

10

0.77826

0.91710

0.89786

0.95330

 

15

0.77497

0.92193

0.89738

0.95250

 

20

0.77497

0.93305

0.90507

0.95267

MSVM-RFE (boost)

5

0.77727

0.92097

0.89848

0.94945

 

10

0.77497

0.93063

0.90108

0.95292

 

15

0.77497

0.92352

0.90133

0.95136

 

20

0.77497

0.92105

0.89957

0.95256

  1. Numbers in parenthesis stands for cutoff value for JOIN method.