Table 6.
Comparison of the prediction performance between our proposed method and other state-of-the-art works on S.cerevisiae dataset
| Method | Feature | Classifier | ACC(%) | SN(%) | PPV(%) | MCC(%) |
|---|---|---|---|---|---|---|
| Our method | MMI+NMBAC | RF | 95.01 ±0.46 | 92.67 ±0.50 | 97.16 ±0.55 | 90.10 ±0.92 |
| You’s work [18] | MLD | RF | 94.72 ±0.43 | 94.34 ±0.49 | 98.91 ±0.33 | 85.99 ±0.89 |
| You’s work [30] | AC+CT+LD+MAC | E-ELM | 87.00 ±0.29 | 86.15 ±0.43 | 87.59 ±0.32 | 77.36 ±0.44 |
| You’s work [16] | MCD | SVM | 91.36 ±0.36 | 90.67 ±0.69 | 91.94 ±0.62 | 84.21 ±0.59 |
| Wong’s work [17] | PR-LPQ | Rotation Forest | 93.92 ±0.36 | 91.10 ±0.31 | 96.45 ±0.45 | 88.56 ±0.63 |
| Guo’s work [12] | ACC | SVM | 89.33 ±2.67 | 89.93 ±3.68 | 88.87 ±6.16 | N/A a |
| Guo’s work [12] | AC | SVM | 87.36 ±1.38 | 87.30 ±4.68 | 87.82 ±4.33 | N/A a |
| Zhou’s work [14] | LD | SVM | 88.56 ±0.33 | 87.37 ±0.22 | 89.50 ±0.60 | 77.15 ±0.68 |
| Yang’s work [15] | LD | KNN | 86.15 ±1.17 | 81.03 ±1.74 | 90.24 ±1.34 | N/A a |
aN/A means not available