Table 3 Performance comparison of different Meta-Classifier Strategies.
From: Using ensemble learning for classifying artistic styles in traditional Chinese woodcuts
Meta-Classifier Strategy | Precision | Recall | F-measure | Accuracy |
|---|---|---|---|---|
Simple Averaging | 0.8816 | 0.8800 | 0.8802 | 88.0000 |
Logistic Regression | 0.8966 | 0.8956 | 0.8955 | 89.5556 |
Single Dense Neural Layer | 0.9092 | 0.9078 | 0.9079 | 90.7778 |
Proposed (CART) | 0.9372 | 0.9367 | 0.9367 | 93.6667 |
