阿基米德的优化算法为基础的特征选择与混合深度学习为基础的流失预测在电信行业
Hanan Abdullah Mengash1, Nuha Alruwais2, Fadoua Kouki3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Biomimetics (Basel, Switzerland)
|January 26, 2024
概括
客户流失预测 (CCP) 使用机器学习 (ML) 预测客户流失. 这项研究引入了一种混合深度学习模型,具有特征选择,在电信客户保留方面达到94.65%的准确性.
科学领域:
- 数据科学和机器学习
- 电信分析 电信分析
背景情况:
- 客户流失预测 (CCP) 对于企业来说至关重要,以保持订阅者并确保利能力.
- 现有的方法经常在电信中与高维数据和最佳功能选择作斗争.
- 深度学习 (DL) 提供了强大的预测模型的潜力,但需要仔细优化.
研究的目的:
- 开发一种高效的混合深度学习模型,用于电信行业的客户流失预测.
- 通过先进的特征选择技术来解决高维度问题.
- 优化模型超参数以提高分类性能.
主要方法:
- 提出了基于阿基米德优化算法的特征选择与基于混合深度学习的流失预测 (AOAFS-HDLCP) 技术.
- 使用阿基米德的优化算法 (AOAFS) 进行最佳的特征选择.
- 在核心预测任务中使用了带有自编码器的卷积神经网络 (CNN-AE).
- 应用热平衡优化 (TEO) 用于CNN-AE模型的超参数调整.
主要成果:
- 与其他方法相比,AOAFS-HDLCP技术显示出更高的性能.
- 达到最高分类准确度为94.65%.
- 通过优化特征选择,有效地减轻了高维度问题.
结论:
- 拟议的AOAFS-HDLCP技术为电信客户流失预测提供了一个强大而高效的解决方案.
- 混合DL方法与先进的优化算法相结合,显著提高了预测准确性.
- 这种方法提高了客户保留策略,并有助于企业的利能力.
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