Research Article
Negative Correlation Learning for Customer Churn Prediction: A Comparison Study
Table 6
Evaluation results (results of best five models are shown in bold).
| Model | Accuracy | Actual churn rate | Hit rate |
| -Nearest neighbour (IBK) | 0.927 | 0.022 | 0.067 | Naive Bayes (NB) | 0.597 | 0.901 | 0.115 | Random Forest (RF) | 0.940 | 0.006 | 0.109 | Genetic programing (GP) | 0.759 | 0.638 | 0.142 | Single ANN with BP | 0.941 | 0.625 | 0.607 | Decision tress (C4.5) | 0.975 | 0.703 | 0.964 | Support vector machine (SVM) | 0.977 | 0.703 | 0.992 | AdaBoosting | 0.972 | 0.719 | 0.898 | Bagging | 0.975 | 0.703 | 0.954 | MLP for cost-sensitive classification (NNCS) | 0.496 | 0.819 | 0.113 | SMOTE + MLP | 0.722 | 0.724 | 0.177 | NCR + CPSO | 0.894 | 0.827 | 0.694 | NCR + MLP | 0.642 | 0.751 | 0.144 | Flat ensemble of ANN | 0.958 | 0.732 | 0.725 | Ensemble of ANN using (NCL) | 0.971 | 0.803 | 0.814 |
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