Variable importance analysis: A comprehensive review

By Pengfei Wei, Zhenzhou Lu, Jingwen Song. Reliability Engineering & System Safety, 2015

469 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Mechanics of Materials, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/eb4001ec14513ad02b4dd14d90f33481209bb652

How researchers see this paper

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

In Management Science and Operations Research

1. Q. Liao
2. Bernd Bischl
3. Kush R. Varshney
4. Christoph Molnar

In Statistics and Probability

1. Q. Liao
2. Bernd Bischl
3. Kush R. Varshney
4. Christoph Molnar
5. Giuseppe Casalicchio

Citations in the corpus

• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “…in both theory and application of SA have been accomplished, as documented in the recent reviews by Norton (2015), Iooss and Lemaître (2015), Wei et al. (2015), Razavi and Gupta (2015), Borgonovo and Plischke (2016), Pianosi et al. (2016), Borgonovo et al. (2017), Borgonovo (2017), Ghanem…”
• SHAPLEY EFFECTS FOR SENSITIVITY ANALYSIS WITH CORRELATED INPUTS: COMPARISONS WITH SOBOL' INDICES, NUMERICAL ESTIMATION AND APPLICATIONS (2017, unknown): “When constructing and using numerical models simulating physical phenomena, global sensitivity analysis (SA) methods are valuable tools [1, 2, 3, 4].”
• Human-Centered Explainable AI (XAI): From Algorithms to User Experiences (2021, background): “How (global model-wide) •Describe the general model logic as feature impact∗, rules† or decisiontrees‡ • If user is only interested in a high-level view, describe what are the top features or rules considered ProfWeight∗†‡ [28], Global feature importance∗ [69, 102], Global feature inspection plots∗ ”
• Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, background): “inally suggested for random forests [13]. Some importance measures rely on removing features from the training data and retraining the model [65]. An alternative are variance-based measures [40]. See [125] for an overview of importance measures. The feature eect expresses how a change in a feature c”