# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 2 Literature Review
### Decision Tree  
The decision tree algorithm is a supervised classification technique used to classify unknown patterns or to create a predictive model to divide subjects into groups or predict the values of a target variable. ID3 is the original decision tree method proposed by [PERSON] [66] who subsequently proposed C4.5. According to [PERSON] and [PERSON] [67], the decision trees (i.e., ID3, CART, and C4.5) have different characteristics when applied to different types of data sets. C4.5 can handle numerical attributes and eliminate the ID3 bias by dealing with missing values and noisy data [68]. The performance of decision trees is determined based on the accuracy and confidence of rules. Accuracy is defined on the basis of a confusion matrix for evaluating classification models.  
Decision trees are widely used in several sectors. [PERSON] and [PERSON] [69] applied decision trees in education to determine the relationship between student satisfaction and their performance in an e-classroom. They proposed five student satisfaction factors: student satisfaction (SATISFACT), grade (GRADE), number of students' attempts to pass the examination (EXAPPROACH), average student responses, and active engagement (views and posts) (EINVOLV), and student workload (WORKLOAD). [PERSON] et al. [70] used a decision tree algorithm to evaluate risk factors associated with vitamin D deficiency. The model investigated 14 variables, with sensitivity, specificity, accuracy, and receiver operating characteristics used for validation. [PERSON] et al. [71] applied decision trees to analyze the influences of internal cognition and the external environment on the loyalty of animations, comics, and games (ACG) consumers. Their results were used to suggest policies concerning products' extensional design, marketing, and CRM in the ACG industry. [PERSON] and [PERSON] [72] used decision trees to identify and classify patterns in the body shapes of soldiers, generating results that are useful for manufacturers to define a suitable range of clothing sizes and to generate regular size patterns to facilitate production. [PERSON] et al. [73] proposed a hybrid system, using C4.5 decision trees and Naive Bayes, to classify customers' interest in an offered product and clusters for product and channel suggestions. The results increased the overall profit/cost ratio. [PERSON] et al. [74] developed a decision tree to identify risk factors associated with hypertension that can be used in programs for hypertension management. [PERSON] et al. [75] applied a decision tree and artificial neural network analysis to analyze historical cases of oral cancer. [PERSON] et al. [76] used a decision tree to predict the soil quality grade for precision fertilization in agriculture.  
Many studies have applied clustering techniques, decision trees derived from customer demographics, and RFM variables. The integration of decision trees and RFM was studied by [PERSON] et al. [40], who developed a customer churn prediction model using a decision tree. LRFMP variables were also considered and found to have an effect on customer churn. [PERSON] et al. [32] analyzed customers' possible responses to specific product promotion. They compared three data analytics techniques: logistic regression, decision trees, and neural networks, and discussed the relative tradeoffs among these in the context of customer segmentation. [PERSON] et al. [77] applied the RFM concept to determine customer loyalty according to the type of customer. Customer loyalty was partitioned into five classes using a K-means clustering algorithm and customer types (platinum, gold, and silver) were heuristically assigned. A classification system using decision trees was used to determine the loyalty of new future customers. [PERSON] et al. [78] applied K-means clustering with RFM for customer segmentation and decision trees, for internet and cable service providers to identify solvent customers who refused to pay after using services.