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Table 2 Comparison of R-SVM and SVM-RFE on Data-S (with sample outliers)

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

Levela ReduceSVb P(sv-diff)c ReduceTestd P(test-diff)e ImproveRecf P(rec-diff)g ReduceOSVh P(osv-diff)i
800 3.25% 4.49E-41 -65.19% 5.65E-36 -10.14% 3.36E-75 50.37% 5.97E-35
600 5.80% 1.90E-57 -70.27% 3.04E-35 -7.14% 5.18E-56 72.28% 1.10E-49
500 7.02% 8.20E-63 -59.63% 1.81E-37 -5.13% 3.37E-39 80.54% 1.17E-56
400 8.26% 1.68E-67 -41.43% 8.31E-25 -2.57% 4.53E-12 89.04% 2.51E-64
300 7.72% 1.20E-58 -19.14% 2.18E-13 0.75% 4.92E-02 93.44% 7.46E-65
200 7.21% 4.54E-51 -6.53% 2.56E-04 4.00% 7.15E-16 93.91% 1.47E-61
150 9.13% 1.29E-71 2.63% 1.20E-01 6.47% 8.41E-23 93.59% 6.27E-61
100 8.30% 1.42E-64 5.56% 8.04E-04 7.69% 3.50E-22 92.44% 1.33E-61
90 8.36% 2.01E-72 4.31% 1.15E-02 6.99% 8.74E-19 91.37% 2.60E-61
80 8.01% 6.63E-71 4.45% 1.99E-02 6.99% 9.33E-18 90.26% 2.65E-60
70 7.17% 1.29E-67 6.59% 3.78E-04 7.52% 2.80E-16 88.56% 7.55E-62
60 6.67% 2.65E-65 6.16% 2.32E-03 7.27% 5.72E-13 86.38% 2.60E-62
50 5.82% 1.08E-58 7.70% 1.34E-04 7.42% 3.71E-12 83.82% 1.23E-61
  1. a,b,c,d,e,f,g same as in Table 1.
  2. h ReduceOS V: Relative reduction in the number of outlier support vectors (the outlier samples being taken as support vectors) in R-SVM comparing to that in SVM-RFE, calculated as: (average #OSVSVM-RFE - average #OSVR-SVM)/(average #OSVSVM-RFE), where #OSV denotes the number of outlier samples being taken as support vectors by the method mentioned in subscript.
  3. i P(osv-diff): The p-value of observed difference in OVS, by paired t-test.