贫穷
随机森林
维数(图论)
排名(信息检索)
特征(语言学)
计算机科学
机器学习
人工智能
优先次序
数学教育
构造(python库)
贫困水平
混乱
统计
计量经济学
数学
心理学
经济增长
经济
管理科学
哲学
程序设计语言
纯数学
语言学
精神分析
作者
Sheng Wang,Yumei Shi,Chengxiang Hu,Chunyan Yu,Shiping Chen
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2023-01-30
卷期号:44 (2): 1769-1779
摘要
Nowadays, poverty-stricken college students have become a special group among college students and occupied a higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this study, a novel model, which incorporated Random Forest with Principle Components Analysis (RF-PCA), is proposed to predict poverty levels of college students. To establish this model, we collect some useful information is to construct the datasets which include 4 classes of poverty levels and 21 features of poverty-stricken college students. Furthermore, the feature dimension reduction consists of two steps: the first step is to select the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF) model; the second step is to extract 11 dimensions by means of Principle Components Analysis (PCA). Subsequently, confusion metrics and receiver operating characteristic (ROC) curves are utilized to evaluate the promising performance of the proposed model. Especially the accuracy of the model achieves 78.61%. Finally, compared with seven states of the art classification algorithms, the proposed model achieves a higher prediction accuracy, which indicates that the results provide great potential to identify the poverty levels of college students.
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