使用鱼优化算法进行SaaS流失预测的新方法方法
Muhammed Kotan1, Ömer Faruk Seymen2, Levent Çallı1
1Department of Information Systems Engineering, Sakarya University, Sakarya, Turkey.
PloS one
|May 13, 2025
概括
软件即服务 (SaaS) 中的客户流量减少,使用鱼优化算法 (WOA) 进行功能选择. WOA优化的数据集提高了SaaS流失模型的预测效率和准确性.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 云计算 云计算 云计算 云计算
背景情况:
- 客户流失对软件即服务 (SaaS) 公司构成重大威胁,影响云计算领域的持续增长.
- 有限的研究存在于SaaS特定的流失模型,特别是关于特征选择和预测算法有效性的研究.
- 有效的流失预测对于知情的管理策略和学术理解至关重要.
研究的目的:
- 引入一种新的方法来预测SaaS中的客户流失,使用鱼优化算法 (WOA) 进行功能选择.
- 在SaaS流失预测中,评估WOA减小数据集与全变量和千平方衍生的数据集的性能.
- 在这些数据集上使用标准性能指标比较各种机器学习算法.
主要方法:
- 使用鱼优化算法 (WOA) 进行了特征选择.
- 创建了三个数据集:WOA缩小,全变量和chi平方衍生,来自一家跨国SaaS公司的用户数据 (>1,000用户).
- 应用和评估的机器学习模型包括k-最近邻居,决策树,天真湾,随机森林和神经网络,并使用AUC,准确性,精度,回忆和F1分数进行评估.
主要成果:
- 与全变量和基平方衍生的数据集相比,减少WOA的数据集显示出更高的预测性能.
- 通过WOA优化功能选择,提高了处理效率.
- 所有测试的机器学习算法在减少WOA的数据集上表现得更好.
结论:
- 鱼优化算法 (WOA) 是SaaS流失预测模型中功能选择的有效方法.
- 基于WOA的功能减少可以提高SaaS环境中的预测准确性和效率.
- 这种方法为SaaS企业提供了有价值的见解,旨在减轻客户流失.
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