Table 5.
Results of Feature Selection for the M_944 Dataset Using the RF and SVM Methods
| Feature Subset | RF |
SVM |
||||||
|---|---|---|---|---|---|---|---|---|
| Acc (%) | MCC | Sn (%) | Sp (%) | Acc (%) | MCC | Sn (%) | Sp (%) | |
| BPB | 68.54 | 0.37 | 69.28 | 67.80 | 71.40 | 0.43 | 75.00 | 67.80 |
| Kmer(2) | 52.22 | 0.04 | 54.45 | 50.00 | 56.78 | 0.14 | 61.65 | 51.91 |
| Kmer(3) | 55.51 | 0.11 | 57.42 | 53.60 | 59.22 | 0.18 | 60.81 | 57.63 |
| Kmer(4) | 56.04 | 0.12 | 58.05 | 54.03 | 58.37 | 0.17 | 59.96 | 56.78 |
| PC-PseDNC-General (2, 0.1) | 53.07 | 0.06 | 56.14 | 50.00 | 57.84 | 0.16 | 64.41 | 51.27 |
| NCP+ND | 67.58 | 0.35 | 70.34 | 64.83 | 68.01 | 0.36 | 69.49 | 66.53 |
| BPB+Kmer(3) | 67.37 | 0.35 | 71.61 | 63.14 | 72.46a | 0.45a | 75.85a | 69.07a |
| BPB+PC-PseDNC-General (2, 0.1) | 67.58 | 0.35 | 70.97 | 64.19 | 71.40 | 0.43 | 73.52 | 69.28 |
| BPB+NCP+ND | 68.43 | 0.37 | 71.82 | 65.04 | 68.11 | 0.36 | 69.70 | 66.53 |
| BPB+PC-PseDNC-General (2, 0.11) + Kmer(3) | 68.33 | 0.37 | 72.67 | 63.98 | 71.72 | 0.44 | 75.00 | 68.43 |
a
Performance with maximum accuracy.