Table 15.
Classification results of proposed ER-VC model for binary classification with SMOTE-balanced train set.
| Feature | Acc. | Class | Prec. | Rec. | F1 |
|---|---|---|---|---|---|
| BoW | 0.94 | Survived | 0.97 | 0.96 | 0.96 |
| Not-survived | 0.79 | 0.83 | 0.81 | ||
| Weighted avg | 0.94 | 0.94 | 0.94 | ||
| TF-IDF | 0.96 | Survived | 0.97 | 0.98 | 0.98 |
| Not-survived | 0.90 | 0.85 | 0.88 | ||
| Weighted avg | 0.96 | 0.96 | 0.96 | ||
| GloVe | 0.86 | Survived | 0.95 | 0.88 | 0.92 |
| Not-survived | 0.53 | 0.74 | 0.62 | ||
| Weighted avg | 0.89 | 0.86 | 0.87 |
GloVe: Global Vectors; ER-VC: Extreme Regression-Voting Classifier; TF-IDF: Term Frequency-Inverse Document Frequency; BoW: Bag of Words; SMOTE: Synthetic Minority Oversampling Approach.