Multiclass classification of microarray data samples with a reduced number of genes

BMC Bioinformatics

Table 1 The classification performance of OAA and ECOC classifiers

						p-values^a
Dataset	M	n	Error-ECOC(F)	Error-OAA(G)	F ≠ G	F < G	MW
200 Montecarlo 4:1 train-test partitions at η = 5
Lymphoma	3	NA	NA	0	NA	NA	-
SRCBT	4	9	0	0	0.00437	0.00219	0.99682
Brain	5	9	0.1250	0.1250	0.98741	-	-
NCI60	8	9	0.3077	0.2308	0.02222	0.01111	0.99682
Staunton	9	12	0.4615	0.4615	0.71123	-	-
GCM RM	11	11	0	0	0.39273	-	-
Su	11	13	0.0857	0.0857	0.92282	-	-
GCM	14	12	0.3625	0.2863	9.99e-16	4.76e-16	1
200 Montecarlo 4:1 train-test partitions at η = 10
Lymphoma	3	11	0	0	0.98741	-	-
SRCBT	4	9	0	0	0.00307	0.00153	0.99999
Brain	5	15	0.1250	0.1250	0.99970	-	-
NCI60	8	14	0.3077	0.2308	0.00213	0.00106	0.99996
Staunton	9	19	0.4615	0.4615	0.79201	-	-
GCM RM	11	12	0	0	0.79201	-	-
Su	11	17	0.0857	0.0857	0.32750	-	-
GCM	14	12	0.3624	0.2863	9.99e-16	4.76e-16	1
200 Montecarlo 4:1 train-test partitions at η = 15
Lymphoma	3	11	0	0	0.98741	-	-
SRCBT	4	9	0	0	0.00307	0.00153	0.99999
Brain	5	18	0.125	0.125	0.99999	-	-
NCI60	8	16	0.3077	0.2308	0.00045	0.00022	0.99999
Staunton	9	19	0.4615	0.4615	0.62717	-	-
GCM RM	11	12	0	0	0.96394	-	-
Su	11	17	0.0857	0.0857	0.46532	-	-
GCM	14	12	0.3666	0.2863	< 2.2e-16	< 2.2e-16	1

The classification performance of ECOC classifiers of size at most ⌈η·log₂M⌉ and OAA classifiers under bounded optimum S 2N gene selection over 200 runs of Montecarlo 4:1 train-test partitions. M and n respectively denote the median number of binary classifiers at ECOC and OAA classifiers. Error-ECOC and Error-OAA respectively denote the median classification errors attained by ECOC and OAA classifiers. Error-ECOC and Error-OAA are denoted as F and G for purposes of KS tests, respectively.
^a p-values of two-sided KS tests, one-sided KS tests and one-sided MW tests. The alternative hypothesis of two-sided KS tests is "the error of ECOC classifiers is different from that of OAA classifiers", i.e., the relationship between CDFs is F ≠ G. The alternative hypothesis for one sided KS tests is "the error of ECOC classifiers is greater than that OAA classifiers", i.e., the relationship between CDFs is F <G. The alternative hypothesis of one sided MW tests is "the error of ECOC classifiers is less than that of OAA classifiers".

ISSN: 1471-2105