# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 3 Motivation  
According to the results of some studies [38; 39; 40], undersampling techniques do not provide a good solution to the class imbalance problem because they may remove some important and useful data from majority classes. This loss of data can result in the performance degradation of a classifier for both majority and minority classes. Therefore, it can be concluded that the performance drop in terms of classification accuracy [28] may stem from the use of undersampling, rather than the unreliability of anthropometric features. To solve this issue, we generated a training dataset by employing the synthetic minority oversampling technique (SMOTE) proposed in [41] to overcome the lack of minority samples. Additionally, for comparison, we generated a training dataset by employing the undersampling technique used in [28].  
Figure 2 illustrates the change of the number of datasets according to the used sampling technique. As shown in Figure 2, the number of samples belonging to the underweight class is significantly lower than the numbers of samples belonging to the other two classes. In this figure, one can see that the original dataset has a class imbalance problem. To overcome this problem, the undersampling technique was employed. Accordingly, some samples were removed from the majority classes. As a result, as shown Figure 2, the normal weight and overweight classes have the same number of samples as the underweight class. Although the class imbalance problem is alleviated by the undersampling technique, the total number of samples in the undersampled training dataset is significantly reduced compared to that in the original dataset. Instead of removing some samples from majority classes, SMOTE augments the samples of minority classes by generating new synthetic samples. As a result, as shown in Figure 2, the number of samples in each minority class is the same as that in the majority class.  
Using the training dataset generated by SMOTE, we trained three machine learning algorithms: C-SVM, nu-SVM and \(k\)-NN. We then tested these algorithms on the testing dataset. It is noteworthy that the performance of average BMI classification for all three schemes improved significantly. \(k\)-NN achieved an accuracy of 92.97%, outperforming the other algorithms.  
Therefore, we can confirm that anthropometric features hold validity for the classification of BMIs. Next, we investigated a method for improving BMI classification accuracy. To this end, we carefully reviewed the generation process for anthropometric features and measured the effects of each feature on classification. Traditionally, anthropometric features have been calculated using the length of each limb and the height of an individual averaged over the given frames. Figure 3 presents the body height of an individual over multiple frames (the height of the fourth subject in the dataset proposed by [PERSON] et al. [28]). As shown in this figure, the values are not consistent between frames as a result of the inaccuracy in pose estimation. Furthermore, there is a specific period during which a large variance among the values can be observed.  
Therefore, if anthropometric features are simply extracted, ignoring such large variance over time, BMI classification accuracy could be significantly reduced. To overcome this problem, it is desirable to use anthropometric features localized over several different time periods rather than using features averaged over all frames. Therefore, in this paper, we propose an ensemble model for BMI classification. In the next section, we discuss our methodology in detail.  
Figure 2: Change of the number of datasets according to sampling technique. Here, the underweight, normal weight and overweight classes are denoted as Classes I, II and III, respectively.