Table 2 Definition of all 44 classification models compared in this work, according to feature sets and learning algorithms. M ij is the classifier induced with the feature set i and algorithm j, i=1,…,12 and j=1,2,3, and w ij is the cross-validation accuracy of the classifier M ij . \(\hat {M}_{ij}\) is the predicted class by \(\text {M}_{ij},\hat {M}_{ij} \in \{-1,1\}\). Emv=Ensemble majority votes, Ewv=Ensemble weighted votes
From: Automatic learning of pre-miRNAs from different species
1. SVMs | 2. RF | 3. J48 | |
|---|---|---|---|
1. FS1 | M11 | M12 | M13 |
2. FS2 | M21 | M22 | M23 |
3. FS6 | M31 | M32 | M33 |
4. FS7 | M41 | M42 | M43 |
5. FS3 | M51 | M52 | M53 |
6. FS4 | M61 | M62 | M63 |
7. FS5 | M71 | M72 | M72 |
8. SELECT | M81 | M82 | M83 |
9. Hyb37 | M91 | M92 | M93 |
10. Hyb S 7 | M101 | M102 | M103 |
11. Hyb 17 | M111 | M112 | M113 |
12. Ss1 | M121 | M122 | M123 |
Emv8 | \(\sum \hat {M}_{i1},i=5,\ldots,12\) | \(\sum \hat {M}_{i2},i=5,\ldots,12\) | \(\sum \hat {M}_{i3},i=5,\ldots,12\) |
Ewv8 | \(\sum w_{i1}\hat {M}_{i1},i=5,\ldots,12\) | \(\sum w_{i2}\hat {M}_{i2},i=5,\ldots,12\) | \(\sum w_{i3}\hat {M}_{i3},i=5,\ldots,12\) |
Emv24 | \(\sum \hat {M}_{ij},i=5,\ldots,12\) and j=1,2,3 | ||
Ewv24 | \(\sum w_{ij}\hat {M}_{ij},i=5,\ldots,12\) and j=1,2,3 | ||