Machine learning and design of experiments with an application to product innovation in the chemical industry

DOI10.1080/02664763.2021.1907840OpenAlexW3147063135MaRDI QIDQ5093046

Rosa Arboretti, Luigi Salmaso, Sara Quaggia, Luca Pegoraro, Catherine Tveit, Riccardo Ceccato, Chris Housmekerides, Sebastiano Vianello, Luca Spadoni, Elisabetta Pierangelo

Publication date: 26 July 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2021.1907840

zbMATH Keywords

artificial neural networksR\&Dproduct developmentrandom forestsexperimental design

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Related Items (1)

Design of experiments and machine learning with application to industrial experiments

Uses Software

• ranger
• VCA

Cites Work

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• Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms
• Big data: some statistical issues
• Efficient point-by-point engine calibration using machine learning and sequential design of experiment strategies
• Which Response Surface Design Is Best: A Performance Comparison of Several Types of Quadratic Response Surface Designs in Symmetric Regions
• Optimal ensemble averaging of neural networks
• Estimation and Accuracy After Model Selection
• BiMM tree: a decision tree method for modeling clustered and longitudinal binary outcomes
• Mixed-effects random forest for clustered data
• Learning representations by back-propagating errors
• Extremely randomized trees
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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