Table 2 LOOCV experiments on robustness of the feature weighting on the spiral with irrelevant noisy feature
From: Feature weight estimation for gene selection: a local hyperlinear learning approach
Spiral data | LHR | I-RELIEF | LOGO | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dimension | SVM | LDA | NB | KNN | HKNN | Aver. | SVM | LDA | NB | KNN | HKNN | Aver. | SVM | LDA | NB | KNN | HKNN | Aver. |
0 | 84.0 | 53.3 | 50.0 | 87.0 | 87.0 | 72.3 | 84.0 | 51.7 | 50.0 | 87.0 | 87.0 | 72.0 | 51.0 | 51.5 | 52.0 | 84.3 | 84.3 | 64.6 |
1000 | 86.0 | 61.2 | 61.8 | 92.0 | 87.0 | 77.6 | 59.3 | 54.3 | 54.8 | 78.3 | 60.5 | 61.4 | 56.8 | 57.7 | 59.3 | 82.3 | 64.3 | 64.1 |
2000 | 90.0 | 69.0 | 67.0 | 92.0 | 89.0 | 81.4 | 56.8 | 57.0 | 55.8 | 72.3 | 57.8 | 59.9 | 57.3 | 57.8 | 60.3 | 88.5 | 83.8 | 69.5 |
3000 | 87.5 | 67.0 | 64.0 | 91.8 | 86.8 | 79.4 | 56.8 | 55.3 | 52.8 | 74.3 | 56.3 | 59.1 | 60.0 | 54.8 | 54.5 | 85.3 | 76.5 | 66.2 |
4000 | 88.5 | 64.0 | 66.3 | 92.3 | 88.5 | 79.9 | 55.5 | 58.8 | 57.3 | 71.3 | 55.3 | 59.6 | 59.0 | 60.5 | 61.8 | 86.5 | 79.5 | 69.5 |
5000 | 89.0 | 67.8 | 66.8 | 92.8 | 87.8 | 80.8 | 81.3 | 59.5 | 57.0 | 85.3 | 77.8 | 72.2 | 61.8 | 60.8 | 63.7 | 88.8 | 81.0 | 71.2 |
6000 | 88.8 | 66.3 | 67.5 | 92.0 | 88.3 | 80.5 | 64.3 | 57.3 | 59.5 | 74.0 | 61.0 | 63.2 | 54.8 | 54.5 | 57.0 | 87.5 | 82.5 | 67.3 |
7000 | 89.3 | 69.5 | 70.0 | 92.0 | 89.0 | 81.9 | 83.5 | 61.0 | 54.8 | 88.0 | 79.5 | 73.3 | 63.0 | 65.5 | 67.0 | 87.8 | 83.8 | 73.4 |
8000 | 86.8 | 65.0 | 66.8 | 93.8 | 88.3 | 80.1 | 0.0 | 56.5 | 59.3 | 69.8 | 55.0 | 48.1 | 66.5 | 67.5 | 69.3 | 89.3 | 82.5 | 75.0 |
9000 | 88.8 | 68.5 | 70.8 | 92.8 | 87.0 | 81.6 | 51.5 | 49.0 | 51.2 | 68.0 | 53.5 | 54.6 | 62.0 | 57.5 | 61.0 | 73.3 | 57.0 | 62.1 |
10000 | 88.8 | 68.5 | 70.8 | 92.8 | 87.0 | 81.6 | 51.5 | 49.0 | 51.2 | 68.0 | 53.5 | 54.6 | 57.0 | 57.5 | 56.0 | 86.5 | 81.8 | 67.8 |