相关实验视频
机器学习模型用于识别恐慌购买情况的重要因素
Md Shahriare Satu1, Md Mahmudul Hasan Riyad2, Tahani Jaser Alahmadi3
1Department of Management Information Systems, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh. shahriarsetu.mis@nstu.edu.bd.
Scientific reports
|October 6, 2025
概括
这项研究引入了一种机器学习模型来检测恐慌购买行为,这在危机期间是一个重大问题. 渐变增强模型在从购买数据中识别这种行为方面是最有效的.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 行为经济学是一种行为经济学.
背景情况:
- 恐慌购买必需品造成市场不稳定和消费者获取危机.
- 关于自动检测恐慌购买行为的研究有限.
- 了解和预测恐慌购买对于社会稳定至关重要.
研究的目的:
- 开发和评估用于预测恐慌购买行为的机器学习模型.
- 解释分类结果并确定导致恐慌购买的关键因素.
- 评估各种分类器在客户购买数据上的表现.
主要方法:
- 收集和预处理的COVID-19客户购买记录.
- 应用合成少数群体过量采样技术 (SMOTE) 变体和特征选择.
- 培训和评估了包括梯度提升在内的最先进的分类器.
- 利用可解释的人工智能 (XAI) 进行模型解释和因素识别.
主要成果:
- 梯度提升及其高级变体在检测恐慌购买行为方面表现出卓越的性能和稳定性.
- 特性选择和SMOTE变体对于平衡数据集至关重要.
- 可解释的AI确定了影响恐慌购买预测的关键因素.
结论:
- 机器学习,特别是渐变增强,为自动化恐慌购买检测提供了强大的解决方案.
- 确定关键的预测因素可以为减轻恐慌购买危机的策略提供信息.
- 这项研究为开发积极的市场稳定工具提供了基础.
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