Skip to main content

Table 8 The comparison of SVM vs. WV on Data-G

From: Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

Levela

ReduceTestd

P(test-diff)e

ImproveRecf

P(rec-diff)g

800

36.36%

1.16E-17

1.02%

2.13E-06

600

38.95%

6.74E-17

9.49%

2.14E-62

500

39.51%

8.72E-21

14.82%

3.77E-71

400

44.84%

3.86E-23

20.83%

1.68E-79

300

49.75%

6.86E-25

28.72%

3.48E-86

200

54.22%

2.02E-27

36.75%

3.70E-91

150

54.83%

9.37E-30

36.14%

2.65E-86

100

43.56%

6.63E-25

33.61%

4.42E-75

90

42.35%

1.85E-26

31.09%

4.23E-73

80

37.37%

7.35E-25

29.08%

3.79E-67

70

32.23%

1.20E-20

26.54%

9.22E-63

60

27.79%

1.16E-20

24.39%

1.24E-61

50

23.47%

8.64E-15

21.80%

1.83E-53

  1. a Level: The number of features selected in each recursive step.
  2. dReduceTest: Relative reduction in the mean test error rates of SVM comparing to that of WV, calculated as: (average TestErrorWV - average TestErrorR-SVM)/(average TestErrorWV).
  3. e P(test-diff): The p-value of the observed differences in test error rates, by paired t-test.
  4. f ImproveRec: Relative improvement in the proportion of recovered informative genes by R-SVM comparing to that by WV, calculated as: (average #RECR-SVM - average #RECWV)/(average #RECWV), where #REC represents the number of recovered true informative genes with the method stated in the subscript.
  5. g P(rec-diff): The p-value of the observed difference in proportion of recovered informative genes, by paired t-test.
Back to article page

BMC Bioinformatics

ISSN: 1471-2105

Contact us