In this paper, a novel machine learning model is proposed to predict the staying time of international migrants. The competitive machine learning approaches which can be used to predict the staying time of international migrants suffer from hyper-attributes tuning and over-fitting issues. Therefore, a particle swarm optimization (PSO) based support vector machine (SVM) model is proposed to predict the staying time of international migrants. Extensive experiments are performed by considering the international migrants dataset to predict the staying time of international migrants. Experimental results illustrate that the proposed approach outperforms the existing machine learning approaches in terms of f-measure, accuracy, specificity, and sensitivity.

中文翻译：

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一种预测国际移民停留时间的新型机器学习模型

在本文中，提出了一种新的机器学习模型来预测国际移民的停留时间。可用于预测国际移民停留时间的竞争性机器学习方法存在超属性调整和过拟合问题。因此，提出了一种基于粒子群优化（PSO）的支持向量机（SVM）模型来预测国际移民的停留时间。通过考虑国际移民数据集进行广泛的实验来预测国际移民的停留时间。实验结果表明，所提出的方法在 f 度量、准确性、特异性和敏感性方面优于现有的机器学习方法。