Using the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification

Table 2 Classification accuracy of the proposed algorithm and alternatives on two subsets of the data in the leave-one-out test.

	BRCA1		BRCA2		PROS		PROS-OUT
	DP	Others	DP	Others	DP	Others	DP	Others
More correlated	18/18	16.67/18	17/17	16.5/17	59/60	53.3/60	12/15	11.83/15
Less correlated	3/4	1.67/4	4/5	1.67/5	34/41	21.67/41	3/6	0.9/6
	DLBCL-FL		ALL-AML		I2000
	DP	Others	DP	Others	DP	Others
More correlated	62/62	58.33/62	38/38	34.67/38	58/58	57.83/58
Less correlated	12/15	9.17/15	0/0	0/0	3/4	1.5/4

The first subset includes those test feature vectors that are more correlated to the samples of the correct class (called, more correlated in this table). The second subset consists of those test feature vectors that are more correlated to the samples of the incorrect class (referred to as less correlated). The proposed approach is superior in both subsets, but especially so in the less correlated category. This is achieved by taking advantage of the information encoded in the test sample.

ISSN: 1471-2105