The Construction of Prediction Model based on Decision Tree-Neural Network Algorithm for Identifying Poverty-Stricken College Students
- DOI
- 10.2991/978-94-6463-040-4_191How to use a DOI?
- Keywords
- Decision Tree-Neural Network; Poverty-Stricken College Students; Prediction
- Abstract
In the context of “three comprehensive education” in campus, the support for economically disadvantaged students in campus is still one of the key tasks that need to be solved. With the limited resources of financial support for poverty-stricken college students, the identification of these students faces serious challenges. In this essay, a prediction model based on decision-tree and neural network algorithm is designed to predict and analyze students’ poverty recognition. After testing and validation, it proves the effectiveness of the model applied to the prediction of college poverty-stricken college students. Based on the author's database, the recognition accuracy of the model can reach 97.52% at present, and the prediction results of student poverty recognition can be realized with data update.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yuncong Zeng AU - Yifan Han PY - 2022 DA - 2022/12/27 TI - The Construction of Prediction Model based on Decision Tree-Neural Network Algorithm for Identifying Poverty-Stricken College Students BT - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022) PB - Atlantis Press SP - 1287 EP - 1293 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-040-4_191 DO - 10.2991/978-94-6463-040-4_191 ID - Zeng2022 ER -