用机器学习融合模型降低ATM网络误报率至0.71%
Enhancing Precision of Automated Teller Machines Network Quality Assessment: Machine Learning and Multi Classifier Fusion Approaches
- 采用堆叠分类器融合随机森林、LightGBM等模型
- 误报率从3.56%降至0.71%，准确率达99.29%
- 适合关注金融系统可靠性与运维优化的机构
保障ATM服务可靠性对现代银行业至关重要，直接影响客户满意度和金融机构运营效率。本文提出一种数据融合方法，重点采用堆叠分类器（Stacking Classifier）整合多种分类模型，以提升ATM网络可靠性。为应对类别不平衡问题，引入合成少数类过采样技术（SMOTE），实现对频繁事件与罕见事件的均衡学习。所提框架将随机森林（Random Forest）、LightGBM和CatBoost模型集成于堆叠分类器中，在测试集上将误报率从3.56%显著降低至0.71%，整体准确率达到99.29%。该多分类器融合方法有效结合各模型优势，大幅减少误报，带来显著成本节约并提升运营决策质量。研究验证了机器学习与数据融合在优化ATM状态检测中的有效性，为金融机构提供可落地、可扩展的ATM网络性能提升方案。
原文摘要 · Abstract (English)
Ensuring reliable ATM services is essential for modern banking, directly impacting customer satisfaction and the operational efficiency of financial institutions. This study introduces a data fusion approach that utilizes multi-classifier fusion techniques, with a special focus on the Stacking Classifier, to enhance the reliability of ATM networks. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, enabling balanced learning for both frequent and rare events. The proposed framework integrates diverse classification models - Random Forest, LightGBM, and CatBoost - within a Stacking Classifier, achieving a dramatic reduction in false alarms from 3.56 percent to just 0.71 percent, along with an outstanding overall accuracy of 99.29 percent. This multi-classifier fusion method synthesizes the strengths of individual models, leading to significant cost savings and improved operational decision-making. By demonstrating the power of machine learning and data fusion in optimizing ATM status detection, this research provides practical and scalable solutions for financial institutions aiming to enhance their ATM network performance and customer satisfaction.
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