# Intelligent Decision Forest Models for Customer Churn Prediction
## 2 Related Works
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [29] highlighted the suitability of CNN for CCP. The suggested CNN had an accuracy of 98% over Multi-Layer Perceptron (MLP). A similar finding was reported by [PERSON], et al. [33] when they deployed CNN for CCP. Also, [PERSON], [PERSON], [PERSON] and [PERSON] [9] utilized SAE for features extraction and LR for CCP. Specifically, SAE is pretrained with its parameters tuned based on Backward Propagation (BP), and then the extracted features are classified by LR. It was observed that the suggested method had a comparable CCP performance. However, there is still room for improvement, particularly in parameter settings. Despite studies indicating that DL approaches are gaining attention and, in some situations, outperforming standard ML methods, the concerns of the system (hardware) reliability and hyper-parameter tweaking are some of its significant constraints. In addition, substantial initiatives have been proposed to boost the efficacy of the baseline ML classifiers via ensemble techniques. [PERSON], et al. [34] proposed stacked ensemble methods using DT, chi-square automatic interaction detection (CHAID), MLP, and kNN with SVM in pairs. The experimental results indicated that the suggested stack ensembles are superior in performance to the individual DT, CHAID, MLP, kNN, and SVM. [PERSON] and [PERSON] [10] compared the performance of selected ensemble methods with baseline classifiers such as SVM, NB, DT, and ANN. Their findings supported the ensemble methods over selected classifiers in terms of performance. [PERSON], et al. [35] deployed stacking and voting ensemble methods on CCP. Initially, a feature grouping operation based on an equidistant measure was deployed by the authors to extend the sample space and reveal hidden data details. The suggested ensemble method was based on DT, LR, and NB. Reports from their study further support the effectiveness of ensemble methods over single classifiers in CCP. [PERSON], et al. [36] implemented ensemble-based NN approaches for CCP. The Bagging, Adaboost, and Majority Voting ensemble methods were developed based on MLP, ANN, and CNN. As observed in their results, in most cases the ensemble-based NN methods are superior to their counterparts. Although the effectiveness of the proposed methods was not correlated with conventional ML methods, the ensemble-based NN approach performed relatively well. In another context, [PERSON], et al. [37] successfully combined clustering algorithms and classification algorithms for CCP. Specifically, four different clustering methods, k-means, x-means, k-medoids, and random clustering, were combined with seven classifiers (kNN, DT, Gradient Boosted Tree (GBT), RF, MLP, NB, and kernel-based NB) based on boosting, bagging, stacking and majority voting. Aside from the ensemble methods being superior, it was also observed that the classification algorithms were better than the clustering algorithms even though the clustering techniques do not have to train any model. Nonetheless, although ensemble approaches have been proposed to accommodate imbalanced datasets, they are not considered a feasible solution to the class imbalance problem. Summarily, numerous CCP models and methods have been suggested, ranging from conventional baseline ML models to advanced methods based on DL, ensemble, and neuro-fuzzy approaches (See Table 1). However, developing new methodologies for CCP is an ongoing research project due to its significance in business research and development, particularly in customer relationship management (CRM). In addition, reports from previous studies have indicated that the class imbalance problem can affect the efficacy of ML-based CCP solutions. Hence, this research presents DF models and their ensemble-based variants for CCP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Platform dependence \\ and parameter setting \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of notable current CCP studies.