10.5281/zenodo.1173888
Long The Nguyen
Irkutsk National Technical University, Lermontov 83, 664074, Irkutsk, Russia
Huong Thu Nguyen
Irkutsk National Technical University, Lermontov 83, 664074, Irkutsk, Russia
Brain Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 5. Flowchart Of Data Classification
Zenodo
2016
Random Forest
3D-model
anthropometry
artificial intelligence
data classification
Random Forest
2016-08-16
en
Figure
https://zenodo.org/record/1173888
10.5281/zenodo.1173887
Creative Commons Attribution 4.0
Open Access
<p>The Random Forest is a powerful classification method because of the following. First, errors are minimized as a result of a random forest, synthesizing through training (learner). The second, random choice at every stage in the Random Forest will reduce the correlation between the learners in the synthesis of the results. In addition, we also found that the total error of layered forest trees depends on their individual errors in forest trees, as well as the correlation between the trees. The article uses the wrapper model (Christopher Tong, 2000) with the objective function for the evaluation, Random Forest algorithm is shown in figure 5. </p>
https://www.edusoft.ro/brain/index.php/brain/article/view/620/680