Journal of Clinical Bioinformatics

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)

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.

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