# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 2 Related Work
### Classification Methods Using Ensemble  
Classification is capable of predicting the category of dependent variables according to the specific criteria of data [13; 14]. A prediction model for classification has little difference depending on data features. There are many different learning methods related to classification. It is important to select a learning method that fits data features. A single learning method has difficulty reflecting the complicated features of data. To solve this problem, an ensemble technique is applied. An ensemble technique uses the given data to merge multiple classifiers and construct a model [15]. It can be divided into Bagging (Bootstrap AGGregING) [16], Boosting [17], and Stacking [18] according to the merging methods of single models. Bagging utilizes the result of Voting, which is the process of extracting samples multiple times, training each model, and totaling a result. In addition, because it adjusts the value extracted from each sample with the use of a median value, it solves the over-fitting problem. Voting is classified into hard-voting, soft-voting, and weighted-voting according to the use of weighting and method of a particular model. Hard-voting is the method of totaling the prediction values of models and selecting the value with the most votes [19]. Soft-voting is the method of adding up probabilities of models and selecting the class with the highest value [20]. Weighted-voting is the method of assigning a different weight to each of the single models in the ensemble. Accordingly, in the ensemble model, none of the models have the same contribution [21]. The random forest learning model is an ensemble model to which the decision tree-based hard-voting Bagging method is applied. Figure 1 shows the bagging structure of the parallel classifier connection using voting. As shown in Figure 1, Bootstrap, the method of recovering and randomly extracting the samples with the same size from the given data, is applied to generate training data. In this manner, each model is trained. The final result is obtained by the voting of the trained model's values. Based on the result, classifiers are connected in parallel for integration [16]. Boosting is a technique for generating a weak classifier the performance of which is similar to that of the classifier drawn by random guessing [17]. Figure 2 shows the boosting technique of using adaptive weights. As presented in Figure 2, during boosting the classifiers run sequentially. In this case, the input data are extracted by Bootstrap, as they are with Bagging. Boosting repeatedly gives a high weight to a wrong answer, and a low weight to a correct answer. Because Boosting improves the error of a previous classifier, it has better performance than Bagging. However, it is easily over-fitted on the input data. In addition, Stacking generates a classification model with the best performance through the merging of multiple classifiers, such as SVM, random forest, and KNN [18]. Figure 3 shows the stacking method using the performance of the classification prediction result. As shown in Figure 3, the Stacking method splits the entire data corpus into test data and training data. The classification result for the test data is drawn from the multiple base models that had learned the training data. Therefore, the training data are reconstructed and the final classifier learns to draw the classification results for the test data. Through test data, a classifier with good performance was used. For this reason, the advantages of each classifier are applied, and their weaknesses are mitigated. Accordingly, studies have been conducted on an appropriate number of single models to improve the performance of ensemble models in diverse fields, including face recognition and remote sensing [15; 22]. [PERSON] et al. [23] proposed an SVM and KNN ensemble model for the detection of traffic accidents. The proposed model uses a single SVM and KNN for traffic accident data. The ensemble model is designed using the Bagging method.