Explainable Machine Learning in Industry 4.0: Evaluating Feature Importance in Anomaly Detection to Enable Root Cause Analysis
Authors:
- Mattia CarlettiMCMattia Carletti
- Chiara MasieroCMChiara Masiero
- Alessandro BeghiABAlessandro Beghi
- Gian Antonio SustoGSGian Antonio Susto
- 1 October 2019
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 21-26
Abstract
No abstract availableThis publication has 14 references indexed in Scilit:
- Predicting Bugs in Software Code Changes Using Isolation Forest2017
- Anomaly Detection Approaches for Semiconductor ManufacturingProcedia Manufacturing, 2017
- High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learningPattern Recognition, 2016
- "Why Should I Trust You?"2016
- Machine learning: Trends, perspectives, and prospectsScience, 2015
- Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional ExpectationJournal of Computational and Graphical Statistics, 2015
- Isolation-Based Anomaly DetectionACM Transactions on Knowledge Discovery From Data, 2012
- A Simple Robust Control Chart Based on MADJournal of Mathematics and Statistics, 2008
- Random ForestsMachine Learning, 2001
- Greedy function approximation: A gradient boosting machine.The Annals of Statistics, 2001