Table 4.
Performance comparison of the proposed and competing methods on the NYU dataset
| Method | ACC (%) | SEN (%) | SPEC (%) | PPV (%) | NPV (%) | |
|---|---|---|---|---|---|---|
| Single cluster (sc) | SVM | 67.34** (ES: 0.58) | 53.33 | 77.32 | 70.71 | 68.38 |
| RSVM | 63.54* (ES: 0.53) | 55.67 | 69.11 | 62.33 | 66.51 | |
| GSVM | 64.24 (ES: 0.38) | 55.67 | 70.71 | 56.90 | 68.54 | |
| DRBM | 68.23* (ES: 0.47) | 59.00 | 74.82 | 69.71 | 70.03 | |
| Multiple cluster (mc) | SVM | 71.97 (ES: 0.34) | 61.00 | 80.00 | 74.93 | 72.02 |
| RSVM | 70.42 (ES: 0.33) | 57.33 | 80.00 | 74.05 | 70.71 | |
| GSVM | 68.10 (ES: 0.31) | 32.67 | 95.71 | 80.83 | 64.94 | |
| DRBM | 75.24 | 61.33 | 85.71 | 82.10 | 73.73 | |
SVM: support vector machine; RSVM: recursive feature elimination‐based SVM; GSVM: graph theory‐based SVM; DRBM: discriminative restricted Boltzmann machine; ACC: accuracy; SEN: sensitivity; SPEC: specificity; PPV: positive predictive value; NPV: negative predictive value. Asterisks represent the results of the Wilcoxon signed‐rank test (*P < 0.05, **P < 0.01) and with the proposed mcDRBM ES denotes the effect size.