Design of experiments and machine learning with application to industrial experiments

被引：10

作者：

Fontana, Roberto ^{[1
]}

Molena, Alberto ^{[2
]}

Pegoraro, Luca ^{[2
]}

Salmaso, Luigi ^{[2
]}

机构：

[1] Politecn Torino, Dept Math Sci, Turin, Italy

[2] Univ Padua, Dept Management & Engn, Padua, Italy

来源：

STATISTICAL PAPERS | 2023年 / 64卷 / 04期

关键词：

Design of Experiments; Machine learning; Active learning; Industrial statistics;

D O I：

10.1007/s00362-023-01437-w

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In the context of product innovation, there is an emerging trend to use Machine Learning (ML) models with the support of Design Of Experiments (DOE). The paper aims firstly to review the most suitable designs and ML models to use jointly in an Active Learning (AL) approach; it then reviews ALPERC, a novel AL approach, and proves the validity of this method through a case study on amorphous metallic alloys, where this algorithm is used in combination with a Random Forest model.

引用

页码：1251 / 1274

页数：24

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