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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.
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BMC Bioinformatics

ISSN: 1471-2105

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