# Intelligent Decision Forest Models for Customer Churn Prediction
## 4 Results and Discussion
### CCP Performance Comparison of Enhanced DFEnsemble Variants, Bagging and Boosting Ensemble Methods
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy (\%)** & **AUC** & **F-Measure** & **MCC** \\ \hline \({}^{\star}\) LMT & 91.59 & 0.896 & 0.876 & 0.752 \\ \hline \({}^{\star}\) FT & 94.26 & 0.973 & 0.942 & 0.883 \\ \hline \({}^{\star}\) RF & 90.32 & 0.880 & 0.503 & 0.806 \\ \hline \({}^{\star}\) WSVEDFM & **96.57** & **0.988** & **0.965** & **0.981** \\ \hline \({}^{\star}\) WSDFM & 96.43 & 0.986 & 0.964 & 0.971 \\ \hline [PERSON] and [PERSON] [59](BBNGA) & 77.50 & - & 0.773 & - \\ \hline [PERSON], [PERSON], [PERSON] and [PERSON] [36] (Bagging) & 80.80 & - & 0.784 & - \\ \hline [PERSON], [PERSON], [PERSON] and [PERSON] [62] (SVM) & 83.70 & - & - & - \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [37] & 94.06 & - & 0.745 & - \\ (KMed+GBT+DL+Dl+Voting) & & & & \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [37] & 94.65 & - & 0.796 & - \\ (KMed+GBT+DL+DL+Stacking) & & & & \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [37] & 94.70 & - & 0.806 & - \\ (KMed+GBT+DL+Dl+Adaboost) & & & & \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [37] & 94.12 & - & 0.746 & - \\ (KMed+GBT+DL+Dl+Bagging) & & & & \\ \hline \end{tabular} \({}^{\star}\) The superior CCP performances are bold and type-faced, and the proposed DF models are designated by an asterisk.  
\end{table} 
Table 16: The CCP performance of proposed DF methods and existing models on Dataset 2. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy (\%)** & **AUC** & **F-Measure** & **MCC** \\ \hline \({}^{\star}\) LMT & 93.60 & 0.971 & 0.966 & 0.872 \\ \hline \({}^{\star}\) FT & 94.83 & 0.975 & 0.968 & 0.897 \\ \hline \({}^{\star}\) RF & 92.14 & 0.943 & 0.945 & 0.843 \\ \hline \({}^{\star}\) WSVEDFM & **96.31** & **0.990** & **0.987** & 0.940 \\ \hline \({}^{\star}\) WSDFM & **96.31** & 0.989 & 0.983 & **0.946** \\ \hline [PERSON] and [PERSON] [59] (BNNGA) & 86.81 & - & 0.688 & - \\ \hline [PERSON], [PERSON] and [PERSON] [60] (SNA + XGBOOST) & - & 0.933 & - & - \\ \hline [PERSON], et al. [67] (CNN+VAE) & 90.00 & - & 0.930 & - \\ \hline [PERSON], [PERSON], [PERSON] and [PERSON] [36] (BaggedMLP) & 94.15 & - & 0.874 & - \\ \hline [PERSON], et al. [68] (LogitBoost) & 85.24 & 0.717 & 0.810 & 0.160 \\ \hline [PERSON] and [PERSON] [69] (P-AGBPNN) & 91.71 & - & 0.951 & \\ \hline [PERSON] and [PERSON] [70] (PFLICM) & 95.41 & - & - & - \\ \hline [PERSON] [71] (Hyper-Parameterized DL with RMSProp) & 86.50 & - & - & - \\ \hline \end{tabular} \({}^{\star}\) The superior CCP performances are bold and type-faced, and the proposed DF models are designated by an asterisk.  
\end{table} 
Table 15: The CCP performance of proposed DF methods and existing models on Dataset 1.  
for CCP. However, the CCP performances of the DF models and their enhanced ensemble variants are superior to these ensemble-based CCP models in most cases. Using another approach, [PERSON], [PERSON] and [PERSON] [60] combined Social Network Analysis (SNA) and XGBoost for CCP. The SNA was deployed to generate new features for the CCP. Also, [PERSON], [PERSON] and [PERSON] [67] enhanced CNN with a Variable Auto-Encoder (VAE). Although their AUC value of SNA+XGBoost is quite significant and the prediction accuracy and f-measure of CNN+VAE is above 90%, there CCP performance are still less than that of the proposed methods. [PERSON] and [PERSON] [70] used a probabilistic-based fuzzy local information c-means (PFLICM) for CCP. PFLICM is a clustering based approach and it had a prediction accuracy value of 95.41%. In addition, [PERSON] [71] hyper-parameterized DL+RMSProp and [PERSON] and [PERSON] [69] designed an adaptive Gain with Back Propagation Neural Networks (P-AGBPNN) for CCP process. These methods are based on enhanced DL techniques with comparable CCP performance. In summary, the proposed DF models and its enhanced ensemble variants are superior to examined existing CCP models with different computational processes on Dataset 1.