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Table 3 The comparisons in seven gene selection methods (gene number = 30).

From: Gene selection algorithm by combining reliefF and mRMR

Feature Selection Method Classifier ALL ARR LYM HBC NCI60 MLL GCM
No feature sel SVM 91.94% 51.04% 95.16% 77.27% 63.33% 97.22% 51.52%
  Naive Bayes 85.23% 49.57% 95.04% 70.11% 45.22% 93.13% 40.33%
mRMR-ReliefF SVM 96.77% 81.43% 100% 95.45% 68.33% 98.61% 64.65%
  Naive Bayes 95.97% 79.05% 100% 95.45% 61.67% 98.61% 61.11%
Maxrel SVM 89.11% 74.53% 100% 72.73% 51.67% 77.78% 60.61%
  Naive Bayes 88.71% 73.49% 100% 63.64% 48.33% 80.56% 46.97%
Information Gain SVM 97.58% 80.13% 98.39% 100% 61.67% 98.67% 46.67%
  Naive Bayes 92.74% 77.21% 93.55% 86.38% 60% 97.22% 47.47%
Sum Minority SVM 93.95% 76.42% 98.39% 95.45% 55% 90.28% 55.05%
  Naive Bayes 91.13% 74.32% 95.16% 81.82% 46.67% 91.67% 49.49%
Twoing Rule SVM 96.77% 79.37% 98.39% 90.91% 61.67% 97.22% 45.96%
  Naive Bayes 90.32% 72.19% 93.55% 86.36% 45% 95.83% 46.46%
F-statistic SVM 97.17% 67.12% 96.77% 90.91% 63.33% 77.22% 39.10%
  Naive Bayes 80.27% 71.55% 98.52% 85.41% 60.15% 80.13% 39.81%
GSNR SVM 93.18% 77.24% 100% 95.45% 63.37% 90.25% 40.74%
  Naive Bayes 90.11% 70.43% 100% 85.65% 58.25% 87.22% 39.81%
  1. This table shows the classification results based on the 30 genes, which are selected from 7 different datasets using seven feature selection methods, named mRMR-ReliefF, Maxrel, information gain, sum minority, twoing rule, F-statistic, GSNR.