Improved shrunken centroid classifiers for high-dimensional class-imbalanced data

Table 4 Performance of the classifiers on real gene expression data sets for the two class classification tasks

Data set	Method	λ ^∗ ^a	# genes	PA	PA₁	PA₂	g-means	AUC
Ivshina	PAM	0	22283	0.84	0.79	0.85	0.82	0.85
(ER)	GM-PAM	4.83	51	0.82	0.91	0.81	0.86	0.90
	ALP	0	22283	0.84	0.82	0.84	0.83	0.86
	GM-ALP	58.24	26	0.85	0.91	0.84	0.87	0.88
	AHP	185.56	20	0.89	0.88	0.89	0.88	0.90
	GM-AHP	69.58	115	0.89	0.88	0.89	0.89	0.91
Wang	PAM	3.71	14	0.61	0.60	0.62	0.61	0.62
	GM-PAM	3.71	14	0.60	0.60	0.62	0.61	0.63
	ALP	8.26	654	0.56	0.57	0.55	0.56	0.63
	GM-ALP	8.26	654	0.56	0.56	0.56	0.56	0.63
	AHP	21.95	135	0.56	0.56	0.56	0.56	0.65
	GM-AHP	21.95	135	0.56	0.56	0.55	0.56	0.63
Korkola	PAM	0.19	7073	0.65	0.71	0.57	0.64	0.64
	GM-PAM	0.19	7073	0.65	0.71	0.57	0.64	0.64
	ALP	4.87	155	0.64	0.65	0.62	0.63	0.64
	GM-ALP	4.87	155	0.69	0.74	0.62	0.67	0.69
	AHP	0.76	1308	0.58	0.68	0.43	0.54	0.58
	GM-AHP	0.76	1308	0.62	0.71	0.48	0.58	0.60

The table reports the same information as Table 1; # genes is the number of active genes. Optimal thresholds were estimated with 5-fold CV and the accuracy measures with LOOCV; see text for details.
^aFor AHP and GM-AHP only λ_θ was optimized while λ_γ was set to zero.