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Table 9 Comparison of the 8 classifiers, for the different experiments with the 100-miRNA signature

From: Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

  

TT vs

 

TCGA

GEO

 

Classifier

TCGA

NT

GEO

(Subtype)

(Subtype)

Global

Gradient Boosting

0.9359

0.9846

0.6697

0.9725

0.8909

0.8907

Random Forest

0.9324

0.9839

0.8085

0.9725

0.8634

0.9121

Logistic Regression

0.9237

0.9799

0.9351

0.9647

0.8476

0.9302

Passive Aggressive

0.8831

0.9606

0.8678

0.9556

0.8197

0.8974

SGD

0.9035

0.9767

0.9393

0.9490

0.8145

0.9166

SVC

0.9154

0.9791

0.7724

0.9451

0.8355

0.8895

Ridge

0.8305

0.9470

0.8867

0.9503

0.8300

0.8889

Bagging

0.9110

0.9812

0.7682

0.9555

0.9070

0.9046

  1. Logistic Regression was the best across all experiments, and Ridge has the worst accuracy