准确识别经济困难学生:数据驱动的方法

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chunyan Yu, Linfeng Gu, Guilin Chen, Aiguo Wang
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引用次数: 0

摘要

大学每年向经济困难的学生提供补贴。然而,传统的方法不能准确地找到那些真正需要帮助的人。近年来,学生经常使用电子卡片进行校园消费,电子卡片的消费数据反映了学生的行为。本文提出了一种将统计方法与机器学习分类算法(SM2L)相结合的数据驱动方法,利用消费数据识别经济困难学生。首先,SM2L提取了关于吃饭和洗澡的七个特征,并根据消费时间和消费数量排除了一些异常消费个体。其次,采用三种分类算法对学生进行分类,通过调整参数得到不同数量的经济困难学生。第三,输出第二阶段的结果与在学校消费更多的学生的交集。实验表明,SM2L能够准确识别经济困难学生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accurate identification of economic hardship students: a data-driven approach
Universities provide subsidies to students with economic hardship every year. However, the traditional method cannot accurately find those who really need help. Recently, students often use e-cards to consume in campus, and the consumption data from e-cards reflect the students' behaviours. In this paper, a data-driven method which combines statistical methods with machine learning classification algorithms (SM2L) is proposed to identify students with economic hardship by using consumption data. First, SM2L extracts seven features about meals and bath and excludes some abnormal consumption individuals according to consumption time and amount. Second, three classification algorithms are used to classify students and get different numbers of students with economic hardship by adjusting the parameters. Third, output the intersection of the result from phase 2 and the students who consume more in school. Experiments show that SM2L can accurately identify students with economic hardship.
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来源期刊
CiteScore
2.00
自引率
0.00%
发文量
69
审稿时长
7 months
期刊介绍: IJAHUC publishes papers that address networking or computing problems in the context of mobile and wireless ad hoc networks, wireless sensor networks, ad hoc computing systems, and ubiquitous computing systems.
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