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Table 5 Comparison of methods by maximum Az value using 8 features (Mass)

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

 

T

MGH

WU

WFUSM

SHH

SVM

 

0.95821

0.97247

0.92252

0.97401

SVM-RFE

 

0.96218

0.97734

0.92252

0.97401

ENSEMBLE

 

0.72102

0.74859

0.67307

0.94292

JOIN (1)

 

0.77944

0.88187

0.79655

0.92650

JOIN (2)

 

0.72102

0.77365

0.79200

0.90262

JOIN (3)

 

0.72102

0.75484

0.79200

0.86857

JOIN (4)

 

0.72102

0.75484

0.75765

0.86861

JOIN (5)

 

0.72102

0.71136

0.67307

0.73745

MSVM-RFE (bootstrap)

5

0.95821

0.97247

0.92423

0.97401

 

10

0.95821

0.97851

0.92288

0.97525

 

15

0.95947

0.97457

0.92288

0.97401

 

20

0.95947

0.97705

0.92315

0.97401

MSVM-RFE (boost)

5

0.95821

0.97247

0.92314

0.97401

 

10

0.95821

0.97616

0.92426

0.97401

 

15

0.95947

0.97247

0.92314

0.97401

 

20

0.95947

0.97387

0.92314

0.97401

  1. Numbers in parenthesis stands for cutoff value for JOIN method.
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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