Table 2

Best testing accuracy and standard errors (mean ± standard error, %) with classification models derived from best training, with the use of GLGS and SVMRFE feature selection algorithms and seven learning classifiers. By using each feature selection algorithm on each data set, the best result as well as the classifier is highlighted in bold.

Learning classifier

GLGS

SVMRFE


Ovarian cancer

Breast cancer

Liver disease

Ovarian cancer

Breast cancer

Liver disease


KNNC

88.0 ± 5.8%

80.5 ± 8.6

88.3 ± 6.3

96.6 ± 2.9

87.9 ± 7.0

95.3 ± 3.4

NBC

79.9 ± 5.3

75.8 ± 9.0

90.8 ± 5.6

90.9 ± 4.5

76.0 ± 9.1

96.5 ± 3.7

NMSC

82.6 ± 5.1

77.8 ± 9.1

92.1 ± 4.4

92.6 ± 3.8

81.8 ± 7.6

96.5 ± 4.0

UDC

82.7 ± 5.4

78.0 ± 8.0

91.3 ± 5.6

92.5 ± 4.4

82.4 ± 7.7

91.7 ± 5.8

SVM_linear

89.6 ± 4.9

85.6 ± 8.3

95.8 ± 3.8

97.9 ± 2.0

89.9 ± 6.0

98.2 ± 2.7

SVM_rbf

90.4 ± 4.3

85.3 ± 7.9

96.4 ± 3.3

98.2 ± 1.8

90.5 ± 6.1

97.5 ± 3.1

LMNN

93.1 ± 4.4

88.3 ± 7.4

97.4 ± 3.2

99.2 ± 1.1

91.7 ± 4.5

99.0 ± 1.8


Liu et al. BMC Genomics 2009 10(Suppl 1):S3   doi:10.1186/1471-2164-10-S1-S3

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