一种基于AIOT的自主混合数据超标采样方法,用于基于行为细分的基于AIOT的流失识别和个性化的建议
Ghulam Fatima1, Salabat Khan1,2, Farhan Aadil1
1Department of Computer Science, Comsats University Islamabad, Attock Campus Pakistan, Attock, Punjab, Pakistan.
PeerJ. Computer science
|January 10, 2024
概括
这项研究将人工智能 (AI) 和物联网 (IoT) 整合到电信客户保留中. 一个统一的平台将流失识别和细分作为一个问题,提高准确性并实现个性化的服务建议.
科学领域:
- 电信 电信服务 电信服务 电信服务
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 电信公司在人工智能和物联网的数字化转型中面临着客户保留挑战.
- 现有的方法往往将流失识别和客户细分作为单独的方法,从而降低了准确性.
- 分析物联网设备数据模式对于了解客户行为和服务包相关性至关重要.
研究的目的:
- 引入一个统一的客户分析平台,用于电信流量识别和细分.
- 为应对将流失和细分作为独立任务的挑战.
- 为了利用人工智能和物联网数据来增强客户保留策略.
主要方法:
- 一个双层优化问题,用于统一的流失识别和细分.
- 自动机器学习 (AutoML) 过量采样,包括SMOTE-NC和SMOTE-ENC,用于不平衡的数据集.
- 用贝叶斯逻辑回归进行因子分析,用于识别细分因子.
主要成果:
- 拟议的统一方法,特别是随机森林与SMOTE-NC,显著优于标准方法.
- 在多个数据集 (IBM,Kaggle Telco,Cell2Cell) 中实现了高精度 (高达94.54%) 和F1得分 (高达81.87%).
- 该方法自主确定集群参数,并确定关键的客户细分因素.
结论:
- 通过统一的分析平台集成AI和物联网,可以提高电信客户的保留率.
- 行为客户细分和个性化建议是关键结果.
- 拟议的双层优化框架为电信中复杂的客户分析提供了强大的解决方案.
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