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