LEADER 05174nam 2200565 450 001 996495169103316 005 20230417135551.0 010 $a3-031-12402-2 035 $a(MiAaPQ)EBC7119942 035 $a(Au-PeEL)EBL7119942 035 $a(CKB)25179515000041 035 $a(PPN)265862787 035 $a(EXLCZ)9925179515000041 100 $a20230306h20222022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInterpretability for Industry 4.0 $estatistical and machine learning approaches /$fAntonio Lepore, Biagio Palumbo, Jean-Michel Poggi, editors 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$dŠ2022 215 $a1 online resource (130 pages) $cillustrations 311 08$aPrint version: Lepore, Antonio Interpretability for Industry 4. 0 : Statistical and Machine Learning Approaches Cham : Springer International Publishing AG,c2022 9783031124013 320 $aIncludes bibliographical references. 320 $aIncludes bibliographical references. 327 $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 -- 1.4.1 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 -- 2.1.3 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 -- 3.2.1.3 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. 327 $a3.2.2.3 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 -- 3.3.3.1 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 -- 4.2.1 GAMLSS Modelling of UK Aggregate Electricity Demand -- 4.2.1.1 Data Overview and Pre-processing -- 4.2.1.2 Interactive GAMLSS Model Building -- 4.3 From GAMs to Aggregations of Experts, Are We Still Interpretable? -- 4.3.1 Online Forecasting with Online Aggregation of Experts -- 4.3.2 Visualizing the Black Boxes -- References. 606 $aIndustry 4.0 606 $aMachine learning$xIndustrial applications 606 $aIndustry 4.0$xStatistical methods 606 $aAprenentatge automātic$2thub 606 $aAplicacions industrials$2thub 608 $aLlibres electrōnics$2thub 615 0$aIndustry 4.0. 615 0$aMachine learning$xIndustrial applications. 615 0$aIndustry 4.0$xStatistical methods. 615 7$aAprenentatge automātic 615 7$aAplicacions industrials 676 $a658.4038028563 702 $aLepore$b Antonio 702 $aPalumbo$b Biagio 702 $aPoggi$b Jean-Michel$f1960- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996495169103316 996 $aInterpretability for Industry 4.0$93057349 997 $aUNISA