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Table 2 Comparison of classification performances on airway data set

From: A filter-based feature selection approach for identifying potential biomarkers for lung cancer

  Classification Algorithms
Feature Selection Methods Support Vector Machine k-Nearest Neighbor Naive Bayes Random Forest
Information Gain 0.6853 (40) 0.8006 (4) 0.8297 (50) 0.8620 (60)
Chi-squared test 0.7052 (20) 0.8029 (60) 0.7997 (3) 0.8309 (50)
Relief-F 0.6633 (25) 0.7825 (9) 0.8329 (25) 0.8685 (60)
t-test 0.6902 (8) 0.7822 (4) 0.8402 (4) 0.8121 (8)
Window t-test 0.6856 (20) 0.7817 (30) 0.8367 (20) 0.8093 (40)
Moderated t-test 0.6878 (6) 0.7875 (5) 0.8329 (5) 0.8115 (20)
BMI 0.7572 (9) 0.8005 (5) 0.8299 (5) 0.8212 (10)
  1. Each value represents the maximum AUC value (via 10-fold cross-validation) achieved by the corresponding feature selection method and classification algorithm. The number of features used to achieve the maximum is shown inside parenthesis.