Evaluation metrics for forecasting methods.

The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of ’slow employment’ increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of ’slow employment’ of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.

大学生就业是关乎国家发展与社会稳定的重要议题。近年来，毕业生人数逐年攀升、就业压力加剧叠加疫情影响，使得“慢就业”现象愈发凸显，成为亟待破解的现实难题。借助数据挖掘与机器学习方法对毕业生就业前景展开分析与预测，可为高校、政府及毕业生提供切实有效的就业指导与服务，是缓解毕业生“慢就业”问题的可行路径。为此，本研究以浙江省2022届1694名高校毕业生的数据为样本，提出了一种基于改进蝙蝠算法（bat algorithm）与支持向量机（support vector machine, SVM）的特征选择预测模型（bGEBA-SVM）。为提升最优特征子集的搜索效率与精度，本文提出一种融合高斯分布策略与淘汰机制的增强型蝙蝠算法，用于优化特征集合。随后将训练数据输入至支持向量机中完成预测。本研究通过与同类方法、知名机器学习模型在IEEE CEC2017基准测试函数、公开数据集及毕业生就业预测数据集上开展对比实验，实验结果表明，bGEBA-SVM可获得更高的预测准确率，最高可达93.86%。此外，继续教育经历、学生干部经历、家庭状况、职业规划以及就业结构是影响就业结果的关键相关特征。综上，bGEBA-SVM可被视为一款性能优异、可解释性强的就业预测模型。