Gives an introduction to interpretability in statistical and machine learning approaches for Industry 4.0
Provides different views in connection with explainability, generalizability and sensitivity analysis
Illuminates interpretability via random forests and flexible generalized additive models
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About this book
Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.
- Generalized Additive Models
- Machine Learning
- Additive Manufacturing Systems
Editors and Affiliations
University of Naples Federico II, Naples, Italy
Antonio Lepore, Biagio Palumbo
University Paris-Saclay, Orsay, France
About the editors
Antonio Lepore is an Associate Professor of Statistics for Experimental and Technological Research (SECS-S/02) in the Department of Industrial Engineering of the University of Naples Federico II.
His research interests and publications in international journals focus on the use of statistical methods for the analysis and monitoring of functional data aimed at the interpretation of complex data coming from high-frequency multi-sensor data acquisition systems.
He is a member of the ENBIS (European Network for Business and Industrial Statistics) and SIS (the Italian Statistical Society).
Biagio Palumbo is an Associate Professor of Statistics for Experimental and Technological Research (SECS-S/02) in the Department of Industrial Engineering of the University of Naples Federico II and President Elect of the European Network for Business and Industrial Statistics (ENBIS).
His research interests are in interpretable statistical learning techniques for industrial engineering and, in particular, for the monitoring of complex data coming from high-frequency multi-sensor acquisition systems and for optimization of manufacturing processes.
He is member of the Italian Statistical Society, the American Society for Quality (ASQ), and the Italian Association of Mechanical Technology.
Jean-Michel Poggi is a Professor of Statistics at Université Paris Cité and a member of the Lab. Maths Orsay (LMO) at Université Paris-Saclay, in France.
His research interests are in nonparametric time series, wavelets, tree-based methods (CART, Random Forests, Boosting) and applied statistics. His work combines theoretical and practical contributions with industrial applications (mainly environment and energy) and software development.
He is Associate Editor of three journals: the Journal of Statistical Software (JSS), Advances in Data Analysis and Classification (ADAC) and the Journal of Data Science, Statistics, and Visualisation (JDSSV).
He is President of the European Network for Business and Industrial Statistics (ENBIS).
Book Title: Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
Editors: Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Softcover ISBN: 978-3-031-12401-3Published: 20 October 2022
eBook ISBN: 978-3-031-12402-0Published: 19 October 2022
Edition Number: 1
Number of Pages: VII, 123
Number of Illustrations: 13 b/w illustrations, 32 illustrations in colour
Topics: Statistical Theory and Methods, Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics in Business, Management, Economics, Finance, Insurance