Table 3 Comparison of the weighted accuracy of different classifiers using i) subsets of our 7 genes and ii) all 25,000 genes
From: A voting approach to identify a small number of highly predictive genes using multiple classifiers
Classifier | Subsets of our 7 genes | All 25,000 genes | ||
|---|---|---|---|---|
| Â | Test set (19) | All data (5-fold CV) | Test set (19) | All data (5-fold CV) |
C4.5 | 84.52% | 88.49% | 79.17% | 62.36% |
C4.5 with boosting (ADABoost) | 91.67% | 89.54% | 63.10% | 62.89% |
C4.5 with bagging | 84.52% | 88.94% | 48.81% | 63.98% |
Naïve Bayes | 84.52% | 92.13% | 50.00% | 52.17% |
Naïve Bayes with bagging | 88.69% | 86.82% | 50.00% | 52.17% |
Naïve Bayes with boosting | 84.52% | 87.65% | 50.00% | 52.17% |
LMT | 84.52% | 88.11% | 77.38% | 60.29% |
NBTree | 84.52% | 83.69% | 66.07% | 58.76% |
Random Forest | 84.52% | 90.59% | 66.07% | 62.47% |
Random Forest with bagging | 88.69% | 90.59% | 73.21% | 64.75% |
Random Forest with boosting | 84.52% | 88.48% | 66.07% | 62.45% |
k-NN | 80.36% | 83.00% | 63.69% | 61.94% |
Logistic Regression | 81.55% | 88.11% | Out of memory* | Out of memory* |
ANN | 77.38% | 83.44% | Out of memory* | Out of memory* |
SVM | 83.33% | 76.23% | 63.69% | 68.12% |