Table 12.
Comparison of best prediction accuracy for the DLBCL dataset.
| Methods (feature selection + classification) | # Selected genes | # Correctly-classified samples (accuracy) |
|---|---|---|
| α depended degree + decision rules | 1 | 48 (78%) |
| [this work] | 2 | 52 (90%) |
| Signal to noise ratios + Weighted voting24 | 13 | 44 (76%) |
| Signal to noise ratios + k-NNs24 | 9 | 41 (71%) |
| Gradient descent algorithm + SVMs24 | unknowne | 45 (78%) |
| HFW + C4.520 | 22 | 44 (76%) |
| HFW + NaiveBayes20 | 19 | 50 (86%) |
| FCBF + C4.520 | 27 | 27 (47%) |
| FCBF + NaiveBayes20 | 27 | 31 (53%) |
| ReliefF + C4.520 | 22 | 25 (43%) |
| ReliefF + NaiveBayes20 | 19 | 31 (53%) |
e
No related data is provided.