School of Accounting, Guangzhou Huashang College, Guangzhou, China.
School of Data Science, Guangzhou Huashang College, Guangzhou, China.
PLoS One. 2022 Oct 13;17(10):e0276006. doi: 10.1371/journal.pone.0276006. eCollection 2022.
Investigation on college students' consumption ability help classify them as from rich or relative poor family, thus to distinguish the students who are in urgent need for government's economic support. As canteen consumption is the main part of the expenses of the college students, we proposed the adjusted K-means clustering methods for discrimination of the college students at different economic levels. To improve the discrimination accuracy, a broad learning network architecture was built up for extracting informative features from the students' canteen consumption records. A fuzzy transformed technique was combined in the network architecture to extend the candidate range for identifying implicit informative variables from the single type of consumption data. Then, the broad learning network model is fully trained. We specially designed to train the network parameters in an iterative tuning mode, in order to find the precise properties that reflect the consumption characteristics. The selected feature variables are further delivered to establish the adjusted K-means clustering model. For the case study, the framework of combining the broad learning network with the adjusted K-means method was applied for the discrimination of the canteen consumption data of the college students in Guangdong province, China. Results show that the most optimal broad learning architecture is structured with 14 hidden nodes, the model training and testing results are appreciating. The results indicated that the framework was feasible to classify the students into different economic levels by analyzing their canteen consumption data, so that we are able to distinguish the students who are in need for financial aid.
调查大学生的消费能力有助于将他们分为富裕或相对贫困家庭，从而区分出急需政府经济支持的学生。由于食堂消费是大学生支出的主要部分，我们提出了一种调整后的 K-均值聚类方法，用于区分不同经济水平的大学生。为了提高判别精度，构建了一个广义学习网络架构，从学生食堂消费记录中提取信息特征。该网络架构中结合了模糊变换技术，以扩大从单一类型消费数据中识别隐含信息变量的候选范围。然后，对广义学习网络模型进行全面训练。我们专门设计了以迭代调优模式训练网络参数，以便找到反映消费特征的精确属性。选择的特征变量进一步用于建立调整后的 K-均值聚类模型。在案例研究中，将广义学习网络与调整后的 K-均值方法相结合的框架应用于中国广东省大学生食堂消费数据的判别。结果表明，最优的广义学习架构具有 14 个隐藏节点，模型的训练和测试结果都非常出色。结果表明，该框架通过分析学生食堂消费数据来对学生进行分类，从而区分出需要经济援助的学生是可行的。
