|
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. |
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| Population size |
Feature size |
The accuracy of different rank methods on the Test data (out-of-sample) [%] |
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|
|
|||||||
| 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. | |||||||
Jirapech-Umpai and Aitken BMC Bioinformatics 2005 6:148 doi:10.1186/1471-2105-6-148 |
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