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Table 1 Comparison of classification performances on MAQC-II 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

0.9031 (6)

0.9380 (25)

0.9008 (40)

0.9206 (50)

Chi-squared test

0.8821 (1)

0.9164 (50)

0.9151 (4)

0.9441 (60)

Relief-F

0.8821 (1)

0.9052 (15)

0.8995 (50)

0.9306 (60)

t-test

0.9067 (15)

0.9100 (20)

0.9042 (8)

0.9304 (40)

Window t-test

0.8903 (5)

0.9216 (5)

0.9012 (2)

0.9199 (10)

Moderated t-test

0.8903 (6)

0.9084 (5)

0.8987 (1)

0.9309 (50)

BMI

0.9077 (4)

0.9298 (15)

0.9164 (4)

0.9250 (9)

  1. Each value represents the maximum AUC value (by 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.