Table 4 Performance evaluation of the algorithms applied to ‘Wine quality_red’ dataset
Algorithm | Accuracy | Sensitivity (Class ‘3’) | Sensitivity (Class ‘5’) | Specificity (Class ‘3’) | Running time (s) | Memory usage (M) |
|---|---|---|---|---|---|---|
C4.5 | 0.9099 | 0.8000 | 0.9266 | 0.9956 | 0.15 | 0.02 |
SVM | 0.6717 | 0 | 0.8062 | 1.0000 | 0.79 | 0.53 |
AdaBoost | 0.6629 | 0 | 0.7871 | 1.0000 | 34.02 | 11.33 |
kNN | 0.8705 | 0.7000 | 0.9178 | 1.0000 | 0.11 | 0.39 |
Naïve Bayes | 0.5604 | 0.3000 | 0.6696 | 0.9975 | 0.00 | 0.01 |
Random forest | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.42 | 10.33 |
Logistic regression | 0.6079 | 0.2000 | 0.7518 | 0.9981 | 0.23 | 0.34 |