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001450569 003__ OCoLC
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001450569 019__ $$a1348481854
001450569 020__ $$a9783031124020$$q(electronic bk.)
001450569 020__ $$a3031124022$$q(electronic bk.)
001450569 020__ $$z9783031124013
001450569 020__ $$z3031124014
001450569 0247_ $$a10.1007/978-3-031-12402-0$$2doi
001450569 035__ $$aSP(OCoLC)1348479084
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001450569 049__ $$aISEA
001450569 050_4 $$aT59.6
001450569 08204 $$a658.4/038028563$$223/eng/20221102
001450569 24500 $$aInterpretability for Industry 4.0 :$$bstatistical and machine learning approaches /$$cAntonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors.
001450569 264_1 $$aCham :$$bSpringer,$$c[2022]
001450569 264_4 $$c©2022
001450569 300__ $$a1 online resource (vii, 123 pages) :$$billustrations (some color)
001450569 336__ $$atext$$btxt$$2rdacontent
001450569 337__ $$acomputer$$bc$$2rdamedia
001450569 338__ $$aonline resource$$bcr$$2rdacarrier
001450569 5050_ $$aIntro -- Preface -- Contents -- 1 Different Views of Interpretability -- 1.1 Introduction -- 1.2 Interpretability: In Praise of Transparent Models -- 1.2.1 What Happened? -- 1.2.2 What Will Happen? -- 1.2.3 What Shall be Done to Make It Happen? -- 1.2.4 Patterns and Models -- 1.3 Generalizability and Interpretability with Industry 4.0 Implications -- 1.3.1 Introduction to Interpretable AI -- 1.3.2 A Wide Angle Perspective of Generalizability -- 1.3.3 Statistical Generalizability -- 1.4 Connections Between Interpretability in Machine Learning and Sensitivity Analysis of Model Outputs
001450569 5058_ $$a1.4.1 Machine Learning and Uncertainty Quantification -- 1.4.2 Basics on Sensitivity Analysis and Its Main Settings -- 1.4.3 A Brief Taxonomy of Interpretability in Machine Learning -- 1.4.4 A Review of Sensitivity Analysis Powered Interpretability Methods -- References -- 2 Model Interpretability, Explainability and Trust for Manufacturing 4.0 -- 2.1 Manufacturing 4.0: Driving Trends for Data Mining -- 2.1.1 Process Monitoring in Manufacturing 4.0 -- 2.1.2 Design of Experiments in Manufacturing 4.0
001450569 5058_ $$a2.1.3 Increasing Trust in AI Models for Manufacturing 4.0: Interpretability, Explainability and Robustness -- 2.2 Additive Manufacturing as a Paradigmatic Example of Manufacturing 4.0 -- 2.3 Increase Trust in Additive Manufacturing: Robust Functional Analysis of Variance in Video-Image Analysis -- 2.3.1 The RoFANOVA Approach -- 2.3.2 An Additive Manufacturing Application -- References -- 3 Interpretability via Random Forests -- 3.1 Introduction -- 3.2 Interpretable Rule-Based Models -- 3.2.1 Literature Review -- 3.2.1.1 Definitions and Origins of Rule Models -- 3.2.1.2 Decision Trees
001450569 5058_ $$a3.2.1.3 Tree-Based Rule Learning -- 3.2.1.4 Modern Rule Learning -- 3.2.2 SIRUS: Stable and Interpretable RUle Set -- 3.2.2.1 SIRUS Algorithm -- 3.2.2.2 Theoretical Analysis -- 3.2.2.3 Experiments -- 3.2.3 Discussion -- 3.3 Post-Processing of Black-Box Algorithms via Variable Importance -- 3.3.1 Literature Review -- 3.3.1.1 Model-Specific Variable Importance -- 3.3.1.2 Global Sensitivity Analysis -- 3.3.1.3 Local Interpretability -- 3.3.2 Sobol-MDA -- 3.3.2.1 Sobol-MDA Algorithm -- 3.3.2.2 Sobol-MDA Properties -- 3.3.2.3 Experiments -- 3.3.3 SHAFF: SHApley eFfects Estimates via Random Forests
001450569 5058_ $$a3.3.3.1 SHAFF Algorithm -- 3.3.3.2 SHAFF Consistency -- 3.3.3.3 Experiments -- 3.3.4 Discussion -- References -- 4 Interpretability in Generalized Additive Models -- 4.1 GAMs: A Basic Framework for Flexible Interpretable Regression -- 4.1.1 Flexibility Can Be Important -- 4.1.2 Making the Model Computable -- 4.1.3 Estimation and Inference -- 4.1.4 Checking, Effective Degrees of Freedom and Model Selection -- 4.1.5 GAM Computation with mgcv in R -- 4.1.6 Smooths of Several Predictors -- 4.1.7 Further Interpretable Structure -- 4.2 From GAM to GAMLSS: Interpretability for Model Building
001450569 506__ $$aAccess limited to authorized users.
001450569 520__ $$aThis volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.
001450569 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 2, 2022).
001450569 650_0 $$aIndustry 4.0.
001450569 650_0 $$aMachine learning$$xIndustrial applications.
001450569 650_0 $$aIndustry 4.0$$xStatistical methods.
001450569 655_0 $$aElectronic books.
001450569 7001_ $$aLepore, Antonio,$$eeditor.
001450569 7001_ $$aPalumbo, Biagio,$$eeditor.
001450569 7001_ $$aPoggi, Jean-Michel,$$d1960-$$eeditor.
001450569 77608 $$iPrint version: $$z3031124014$$z9783031124013$$w(OCoLC)1332779987
001450569 852__ $$bebk
001450569 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-12402-0$$zOnline Access$$91397441.1
001450569 909CO $$ooai:library.usi.edu:1450569$$pGLOBAL_SET
001450569 980__ $$aBIB
001450569 980__ $$aEBOOK
001450569 982__ $$aEbook
001450569 983__ $$aOnline
001450569 994__ $$a92$$bISE