# A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea
## 3 Materials and Methods
### Competing Methods  
Once the feature selection process is completed, we prepare the dataset for model training. For this study, we selected well-known machine learning models, such as kNN and decision tree, ensemble learning models, such as RF and XGBoost, and deep learning models, such as CNN and CNN-LSTM. We selected these models based on their performance in terms of prediction accuracy and processing speed. This section describes each model in detail.  
kNN is a well-known model for the classification of data points. It first calculates the distances between the current point and other points. Based on the calculated distances, kNN makes classification decisions by checking the closest \(k\) number of points [38]. Although the kNN model is relatively simple compared with other models, it is sensitive to the value of \(k\). In Section 4.2, we will demonstrate how to choose the optimal \(k\) based on the cross-validation technique. The decision tree is another well-known model that classifies the data points using a tree structure. It initially starts with a root node that connects the next decision node or terminal node based on certain conditions. Here, a root or decision node represents an input feature, and a terminal node represents an output label. The decision tree is widely used in many applications (e.g., banking applications for loan default prediction) due to its speed and easy-to-understand structure. However, the decision tree suffers from several disadvantages that, in certain cases, may lead to a  
Figure 4: Correlation matrix between brand experience factors and customer churn risk and customer satisfaction factors.  
complex tree structure and overfitting and underfitting problems. The ensemble learning models can overcome the disadvantages of classification models (i.e., overfitting and underfitting, noise handling, and low accuracy). There are mainly two kinds of ensemble learning models: bagging and boosting. The models, which use the bagging technique, train several models parallelly and output average of the result from each model. A representative model that uses the bagging technique is RF. On the other hand, the models, which use the boosting technique, train several models sequentially and improve the next model by the error of the previous model. A representative model that uses the boosting technique is XGBoost [39, 40].  
CNN is a deep learning model most commonly applied to analyze computer vision tasks such as image and video. However, CNN is also used in classification or regression tasks with tabular data [41]. The structure of the CNN model includes a convolution layer, pooling layer, flatten (i.e., fully connected) layer, and one more dense layer. Here, the convolution layer creates a feature matrix and filters them to compute the input of the next pooling layer. The pooling layer is used to reduce feature dimensions to save processing time. Next, the flatten layer transforms the multi-dimensional output to a one-dimensional input of the next layer. Finally, the dense layer returns the corresponding label of the input. Recently, there have been several applications (e.g., [42]) that have used a combination of CNN and LSTM to improve the accuracy of the data classification task. Likewise, we used the CNN-LSTM model to improve the prediction accuracy of customer churn risk and satisfaction. The main feature of the CNN-LSTM model is that it modifies the underlying structure of CNN by preserving its features. Specifically, the flatten layer in CNN is used to convert data dimensions into one dimension. Instead of converting the data dimensions, we replaced it with an LSTM layer. The LSTM layer computes the correlation between features, and thus, it can return a more meaningful output.