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Table 6 Comparison of methods by maximum Az value using 8 features (Calcification)

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

  T MGH WU WFUSM SHH
SVM   0.91182 0.98765 0.94690 0.96595
SVM-RFE   0.91196 1.00000 0.95100 0.96595
ENSEMBLE   0.53915 0.69512 0.56583 0.91392
JOIN (1)   0.67508 0.71655 0.83947 0.93422
JOIN (2)   0.57971 0.72941 0.76157 0.88733
JOIN (3)   0.57971 0.72941 0.62686 0.73542
JOIN (4)   0.54571 0.69512 0.62686 0.72464
JOIN (5)   0.54571 0.69512 0.54077 0.66210
MSVM-RFE (bootstrap) 5 0.91182 0.98765 0.95326 0.97868
  10 0.91182 0.98765 0.95168 0.96595
  15 0.91182 0.98765 0.94690 0.96757
  20 0.91182 0.98765 0.94690 0.97348
MSVM-RFE (boost) 5 0.91182 0.98765 0.94690 0.96595
  10 0.91182 0.99259 0.94690 0.96595
  15 0.91182 0.99429 0.94690 0.96595
  20 0.91182 0.98765 0.94690 0.96595
  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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