对于工业批量处理的成本敏感决策支持
Simon Mählkvist1,2, Jesper Ejenstam1, Konstantinos Kyprianidis2
1Kanthal AB, 73427 Hallstahammar, Sweden.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究为批量数据分析开发了成本敏感的决策支持. 随机森林分类器实现了26%的成本降低,改善了多过程批量数据系统分析.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 工艺工程是过程工程.
背景情况:
- 批处理过程产生复杂的数据,需要复杂的分析,以提供有效的决策支持.
- 现有的方法很难在批量数据决策中平衡准确性和覆盖率.
- 集成传感器和时间序列数据在多过程系统中带来了挑战.
研究的目的:
- 为多过程批量数据开发一个成本敏感的决策支持系统.
- 在这种情况下,评估不同机器学习分类器的性能.
- 使用成本指标优化预测准确度和覆盖范围之间的权衡.
主要方法:
- 批量数据分析 (BDA) 具有批量数据结构和特征适应.
- 逻辑回归,随机森林分类器和支持向量机的实现和比较.
- 应用成本敏感学习和成本矩阵来汇总准确度-覆盖权衡.
- 开发两个场景来处理覆盖范围之外的批次 (丢弃或处理).
主要成果:
- 随机森林分类器的性能优于物流回归和支持向量机.
- 与基线相比,开发的系统显示相对成本降低了26%.
- 概率估计允许过低概率预测,管理准确度-覆盖权衡.
结论:
- BDA,功能适应和成本敏感学习的协同作用提供了有效的成本意识决策支持.
- 随机森林分类器是一个适合在多过程批量数据系统中增强决策的模型.
- 该方法为分析复杂的批量流程和优化运营成本提供了有价值的框架.
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