Table 3.

Comparison between different feature selection techniques and the proposed ensemble method for k=100, on the TCGA dataset

Classifier	Random	GALGO	EFS-CLA	UFS	EN	LASSO	RFE	EFS
Gradient Boosting	0.8588	0.8782	0.8871	0.9028	0.9208	0.9315	0.9309	0.9359
Random Forest	0.8515	0.8787	0.8824	0.8929	0.9224	0.9341	0.9288	0.9324
Logistic Regression	0.8015	0.8295	0.8832	0.8813	0.8988	0.8996	0.9088	0.9237
Passive Aggressive	0.6986	0.7235	0.8111	0.8091	0.8406	0.8424	0.8506	0.8831
SGD	0.7278	0.764	0.8446	0.8334	0.8649	0.8648	0.8824	0.9035
SVC	0.8077	0.8348	0.8706	0.885	0.9049	0.9008	0.9103	0.9154
Ridge	0.6534	0.6614	0.7422	0.7504	0.7753	0.7751	0.7954	0.8305
Bagging	0.822	0.8382	0.8562	0.8719	0.8889	0.9078	0.9061	0.911
Global Average	0.7777	0.8010	0.8472	0.8534	0.8771	0.8820	0.8892	0.9044
Calls to Classifier	-	60,000	480	-	-	10	946	80