Table 2.
The average accuracy using out-of-sample prediction on the 34 leukemia test samples. The symbol (*) means that there is some perfect predictors found by the algorithm. The highest accuracy is written in bold.
| Population size | Feature size | The accuracy of different rank methods on the Test data (out-of-sample) [%] | |||||
| R1 | R2 | R3 | R4 | R5 | R6 | ||
| 10 | 30 | 97.35* | 95.29 | 93.82 | 92.94* | 93.53 | 94.12 |
| 50 | 98.24* | 95.59 | 94.71* | 93.82 | 93.53 | 95.00 | |
| 30 | 30 | 96.74* | 92.65 | 94.41 | 95.00* | 94.71 | 93.82 |
| 50 | 97.06* | 95.00 | 95.00 | 93.82 | 95.30 | 93.82 | |
| 50 | 30 | 97.35* | 93.82 | 94.71* | 92.06* | 94.12* | 93.82* |
| 50 | 96.17 | 93.82 | 92.65 | 92.35 | 94.12 | 94.71 | |
Abbreviations: R1. Information gain; R2. Twoing rule; R3. Gini index; R4. Sum minority; R5. Max minority; R6. Sum of variances.