Interpretation of nonlinear relationships between process variables by use of random forests

By Lidia Auret, C. Aldrich. 2012

237 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Mechanical Engineering, Optimization & Training, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Vaishak Belle
2. Ioannis Papantonis
3. Timo Speith
4. Hector J. Levesque
5. Christian Walter Peter Omlin
6. Waddah Saeed
7. Longo Luca
8. Giulia Vilone

In Control and Systems Engineering

1. Vaishak Belle
2. Ioannis Papantonis

In Optimization & Training

1. Vaishak Belle

In Safety, Robustness & Interpretability

1. Vaishak Belle
2. Ioannis Papantonis
3. Longo Luca
4. Giulia Vilone

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Following this contribution [241] shows, in an real setting, how the usage of variable importance reflects the underlying relationships of a complex system modeled by a Random Forest.”
• Principles and Practice of Explainable Machine Learning (2020, background): “The authors in [5] propose a way to determine a threshold for identifying important features.”