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 | 83.0 | 83.0 | 51.3 | 83.8 | 52.5 | 75.2 | 81.0 | 81.0 | 51.5 | 82.5 | 51.5 | 75.4 | 53.0 | 52.5 | 53.0 | 80.3 | 80.3 | 63.8 |
1000 | 86.0 | 61.2 | 61.8 | 92.0 | 87.0 | 77.6 | 59.8 | 53.8 | 55.0 | 57.7 | 59.0 | 57.0 | 56.0 | 59.3 | 60.7 | 63.7 | 65.8 | 61.1 |
2000 | 90.0 | 69.0 | 67.0 | 92.0 | 89.0 | 81.4 | 57.0 | 54.0 | 58.0 | 57.3 | 57.5 | 56.8 | 57.0 | 58.5 | 59.5 | 80.3 | 83.0 | 67.7 |
3000 | 87.5 | 67.0 | 64.0 | 91.8 | 86.8 | 79.4 | 54.5 | 55.0 | 53.3 | 49.5 | 54.5 | 53.3 | 56.3 | 55.0 | 55.8 | 77.8 | 76.8 | 64.3 |
4000 | 88.5 | 64.0 | 66.3 | 92.3 | 88.5 | 79.9 | 55.8 | 56.5 | 58.3 | 51.7 | 56.0 | 55.6 | 59.3 | 62.8 | 61.5 | 77.5 | 79.8 | 68.2 |
5000 | 89.0 | 67.8 | 66.8 | 92.8 | 87.8 | 80.8 | 79.3 | 59.8 | 55.0 | 75.8 | 78.0 | 69.5 | 62.0 | 62.3 | 62.7 | 83.0 | 82.7 | 70.5 |
6000 | 88.8 | 66.3 | 67.5 | 92.0 | 88.3 | 80.5 | 65.8 | 55.8 | 60.0 | 60.8 | 63.0 | 61.0 | 54.0 | 51.0 | 56.0 | 79.2 | 81.3 | 64.3 |
7000 | 89.3 | 69.5 | 70.0 | 92.0 | 89.0 | 81.9 | 83.8 | 60.8 | 55.8 | 79.7 | 78.0 | 71.6 | 67.3 | 63.2 | 67.3 | 81.5 | 83.5 | 72.5 |
8000 | 86.8 | 65.0 | 66.8 | 93.8 | 88.3 | 80.1 | 55.0 | 58.5 | 57.0 | 53.5 | 52.5 | 55.3 | 66.8 | 64.8 | 66.5 | 78.5 | 83.3 | 72.0 |
9000 | 88.8 | 68.5 | 70.8 | 92.8 | 87.0 | 81.6 | 56.0 | 51.2 | 53.0 | 54.8 | 54.0 | 53.8 | 64.0 | 59.0 | 61.3 | 55.5 | 58.5 | 59.7 |
10000 | 88.8 | 68.5 | 70.8 | 92.8 | 87.0 | 81.6 | 84.0 | 57.5 | 56.3 | 82.5 | 83.3 | 72.7 | 59.8 | 57.3 | 57.8 | 78.5 | 81.5 | 67.0 |