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用于织物质量预测的自动机器学习:比较分析

Ahmet Metin1, Turgay Tugay Bilgin1

  • 1Bursa Technical University, Bursa, Turkey.

PeerJ. Computer science
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PubMed
概括
此摘要是机器生成的。

本研究评估了使用工业4.0数据进行织面料质量预测的七种自动机器学习 (AutoML) 工具. 在不同的准确度指标上,EvalML和AutoGluon在平衡效率和预测准确度方面表现出卓越的表现.

关键词:
在AutoML中使用AutoML.功能重要性 功能重要性超参数优化超参数优化不平衡的数据不平衡的数据模型的解释性 模型的解释性质量控制 质量控制织工业 织行业 织行业 织行业

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科学领域:

  • 织制造业 织制造业 织制造业 织制造业 织制造业
  • 机器学习 机器学习
  • 工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是什么?

背景情况:

  • 在织制造业中,布料质量预测至关重要.
  • 物联网 (IoT) 和企业资源规划 (ERP) 系统提供了宝贵的数据.
  • 工业4.0整合提高了生产率,并缩短了交付时间.

研究的目的:

  • 为了应对在织物质量预测中的不平衡数据挑战.
  • 评估七个开源自动机器学习 (AutoML) 技术.
  • 确定最佳的AutoML解决方案,平衡计算效率和预测准确度.

主要方法:

  • 评估了七个AutoML工具:FLAML,AutoViML,EvalML,AutoGluon,H2OAutoML,PyCaret和TPOT. 这三种工具的使用情况.
  • 开发一种创新的方法,在计算效率和预测准确性之间达成妥协.
  • 分析特征重要性排名的模型可解释性.

主要成果:

  • 对于特定的目标函数,EvalML表现最好,在平均绝对误差 (MAE) 中表现出色.
  • 尽管推断时间较长,但AutoGluon在平均绝对百分比误差 (MAPE),根平均平方误差 (RMSE) 和R平方方面表现出卓越的表现.
  • 对于具有众多唯一值的分类变量,sin/cos编码被证明是有效的.

结论:

  • 自动ML技术为提高织行业的织物质量预测提供了巨大的潜力.
  • 平衡预测准确性和计算效率是实际AutoML实现的关键.
  • 特性重要性分析提高了模型的解释性,指导未来的研究和工业4.0的采用.