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

BMC Bioinformatics

Table 2 Multi-class classification results for the class-imbalanced scenario in the alternative case

Method	λ ^∗ ^a	#, % info	# non-info [%]	FDR	PA ₁	PA ₂	PA ₃	g-means
					(n ₁ *= 100)*	(n ₁ *= 20)*	(n ₃ *= 100)*
PAM	6.6	59.23	0 [0.00]	0	0.86	0.04	0.86	0.29
	(0.58)	(33.68)	(0)	(0.00)	(0.02)	(0.02)	(0.02)	(0.05)
GM-PAM	1.4	99.64	834.56 [17.03]	0.5	0.75	0.31	0.75	0.55
	(0.96)	(5.75)	(1262.68)	(0.41)	(0.04)	(0.06)	(0.04)	(0.04)
ALP	58.76	99.98	49 [1.00]	0.01	0.82	0.15	0.82	0.46
	(15.12)	(0.14)	(490)	(0.1)	(0.04)	(0.05)	(0.03)	(0.04)
GM-ALP	12.77	99.64	415.68 [8.48]	0.19	0.74	0.34	0.74	0.57
	(12.88)	(3.6)	(1330.05)	(0.31)	(0.05)	(0.08)	(0.05)	(0.05)
AHP	59.05	99.99	98 [2.00]	0.02	0.82	0.15	0.82	0.45
	(15.74)	(0.1)	(689.46)	(0.14)	(0.04)	(0.05)	(0.04)	(0.05)
GM-AHP	13.55	100	316.73 [6.46]	0.17	0.74	0.34	0.74	0.57
	(13.05)	(0)	(1164.99)	(0.28)	(0.05)	(0.08)	(0.05)	(0.04)

The table reports the same information as Table 1; # non-info [%] was selected out of 4,900 non-informative variables and #, % info was selected out of 100 informative variables; see text for details.
^aFor AHP and GM-AHP only λ_θ was optimized while λ_γ was set to zero.

ISSN: 1471-2105