Table 2 Classification accuracy of \(\text {NeurDNet}\) in the two cases of employing binary and probabilistic features.
From: A deep explainable artificial intelligent framework for neurological disorders discrimination
Classifier | Binary features | Probabilistic features | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
25% | 35% | 45% | 55% | 65% | 75% | 25% | 35% | 45% | 55% | 65% | 75% | |
RF (entropy) | 85.69 | 84.24 | 82.91 | 81.94 | 82.43 | 78.68 | 86.18 | 85.43 | 83.79 | 82.66 | 82.20 | 78.21 |
RF (gini) | 85.43 | 84.59 | 83.43 | 82.35 | 81.97 | 78.28 | 86.49 | 84.81 | 84.27 | 82.63 | 82.57 | 78.29 |
SVM (rbf) | 85.68 | 84.65 | 84.24 | 82.19 | 83.10 | 79.46 | 86.33 | 85.83 | 85.38 | 82.09 | 82.68 | 79.01 |
SVM (linear) | 84.26 | 82.69 | 82.08 | 81.34 | 80.78 | 78.02 | 85.83 | 84.77 | 83.60 | 82.36 | 82.02 | 78.57 |
NB | 83.70 | 83.55 | 80.23 | 81.44 | 81.67 | 77.31 | 85.98 | 86.42 | 84.94 | 83.94 | 84.15 | 81.48 |
LR | 85.76 | 84.41 | 84.09 | 83.10 | 82.83 | 79.49 | 87.29 | 86.10 | 85.28 | 83.65 | 83.38 | 79.74 |
AdaBoost | 83.97 | 81.61 | 80.99 | 79.95 | 79.30 | 75.80 | 85.03 | 82.97 | 81.53 | 80.01 | 78.12 | 73.32 |
LDA (svd) | 79.54 | 76.25 | 75.83 | 73.79 | 66.21 | 67.44 | 77.81 | 76.41 | 76.56 | 72.31 | 65.12 | 63.62 |
LDA (lsqr) | 79.54 | 76.25 | 75.80 | 73.77 | 63.40 | 49.57 | 77.81 | 76.41 | 76.56 | 72.31 | 65.12 | 49.50 |
QDA | 81.85 | 83.18 | 78.69 | 72.08 | 63.26 | 58.62 | 95.55 | 93.89 | 81.73 | 73.48 | 56.29 | 53.13 |
DT (entropy) | 81.21 | 78.45 | 77.66 | 77.63 | 76.02 | 74.75 | 80.40 | 79.01 | 77.11 | 77.57 | 75.06 | 71.73 |
DT (gini) | 80.45 | 80.16 | 78.51 | 77.25 | 77.32 | 75.25 | 77.99 | 78.29 | 76.89 | 76.35 | 74.29 | 71.84 |
MLP (10) | 85.01 | 82.40 | 82.05 | 81.25 | 79.79 | 77.53 | 84.33 | 83.03 | 81.64 | 80.25 | 80.04 | 77.04 |
MLP (30) | 84.64 | 82.84 | 82.02 | 80.85 | 79.63 | 77.49 | 84.53 | 82.80 | 81.79 | 80.50 | 80.33 | 77.45 |
