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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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