Table 9.

Averaged AUC values for different methods

Classifier	Decision Tree	Random Forest	GA with Rotation Forest	BFA + RF	SVM	SVM
Kernel					Linear kernel	RBF kernel
GSE32394	0.7589 ± 0.2256	0.8000 ± 0.2449	0.7000 ± 0.3317	0.8000 ± 0.2449	0.9644 ± 0.0422	0.9344 ± 0.0456
GSE59993	0.8099 ± 0.0740	0.7484 ± 0.1438	0.8663 ± 0.0983	0.8474 ± 0.1381	0.8371 ± 0.0331	0.8287 ± 0.0247
GSE1872	1.0000 ± 0.0000	0.9951 ± 0.0178	0.9667 ± 0.1000	1.0000 ± 0.0000	0.3977 ± 0.2008	0.2042 ± 0.0686
GSE76260	0.8313 ± 0.0813	0.7889 ± 0.0441	0.8583 ± 0.0500	0.8167 ± 0.1856	0.7857 ± 0.0629	0.8357 ± 0.0213
GSE59246	0.6455 ± 0.0795	0.8486 ± 0.0349	0.8474 ± 0.1026	0.7646 ± 0.1304	0.8896 ± 0.0375	0.7629 ± 0.0094
BRCA1	0.9925 ± 0.0115	0.9727 ± 0.4166	0.9818 ± 0.3636	0.9909 ± 0.2727	0.9598 ± 0.0317	0.9918 ± 0.0060
BRCA2	0.9997 ± 0.0026	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000
BRCA3	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	0.9997 ± 0.0026	1.0000 ± 0.0000
Classifier	SVM	MKL(d = 3)	MKL(d = 3)	MKL(d = 3)	MKL(d = 21)	HMKL
Kernel	Hadamard kernel	RBF kernel	Hadamard kernel	Mixed kernels	Mixed kernels
GSE32394	0.9778 ± 0.0222	0.9422 ± 0.0422	0.9844 ± 0.0511	0.9867 ± 0.6333	0.9899 ± 0.0333	0.9933 ± 0.0378
GSE59993	0.8661 ± 0.0510	0.7073 ± 0.0532	0.8973 ± 0.0445	0.8990 ± 0.0336	0.9018 ± 0.0175	0.9069 ± 0.0178
GSE1872	1.0000 ± 0.0000	0.2667 ± 0.0894	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000
GSE76260	0.8595 ± 0.0126	0.8302 ± 0.0419	0.8467 ± 0.0313	0.8604 ± 0.0416	0.8633 ± 0.0313	0.8735 ± 0.0190
GSE59246	0.8996 ± 0.0250	0.8939 ± 0.0317	0.8991 ± 0.0179	0.9006 ± 0.0292	0.9008 ± 0.0282	0.9048 ± 0.0047
BRCA1	0.9953 ± 0.0047	0.9921 ± 0.0061	0.9953 ± 0.0045	0.9957 ± 0.0032	0.9960 ± 0.0026	0.9967 ± 0.0027
BRCA2	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000
BRCA3	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000	1.0000 ± 0.0000