Explainable Machine Learning in Industry 4.0: Evaluating Feature Importance in Anomaly Detection to Enable Root Cause Analysis.
Mattia CarlettiChiara MasieroAlessandro BeghiGian Antonio SustoPublished in: SMC (2019)
Keyphrases
- anomaly detection
- root cause analysis
- feature importance
- machine learning
- network anomaly detection
- intrusion detection
- random forest
- feature selection
- root cause
- detecting anomalies
- anomalous behavior
- network traffic
- decision support
- unsupervised learning
- learning algorithm
- detect anomalies
- one class support vector machines
- decision trees
- machine learning methods
- intrusion detection system
- natural language processing
- network intrusion detection
- machine learning algorithms
- semi supervised
- knowledge discovery
- negative selection algorithm
- computer vision
- data mining
- text classification
- data sets
- semi supervised learning
- model selection
- principal component analysis
- relevance feedback
- information extraction
- support vector machine
- active learning
- data analysis
- reinforcement learning