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Table 4 The performance of OAA and ECOC classifiers on train-test partitions

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

Dataset

M

n

G-ECOC

G-OAA

Error-ECOC

Error-OAA

   

η = 5, 10, 15

  

GCM RM

11

10

926

1260

0.1852

0.1852

GCM

14

20

1314

423

0.4782

0.3043

  1. The performance of OAA and ECOC classifiers of size at most ⌈η·log2M⌉ on benchmark microarray datasets under bounded optimum S 2N gene selection and a public train-test partition. M and n respectively denote the number of binary classifiers used by OAA and ECOC classifiers. G-OAA and G-ECOC respectively denote the overall number of genes selected by OAA and ECOC classifiers. Error-OAA and Error-ECOC respectively denote the classification error attained by OAA and ECOC classifiers.
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BMC Bioinformatics

ISSN: 1471-2105

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