Open Access
UniformLIME: A Uniformly Perturbed Local Interpretable Model-Agnostic Explanations Approach for Aerodynamics
Author(s) -
Enshuo Jiang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2171/1/012025
Subject(s) - interpretability , aerodynamics , fidelity , computer science , artificial intelligence , range (aeronautics) , machine learning , field (mathematics) , sample (material) , mathematics , engineering , aerospace engineering , physics , telecommunications , pure mathematics , thermodynamics
Machine learning and deep learning are widely used in the field of aerodynamics. But most models are often seen as black boxes due to lack of interpretability. Local Interpretable Model-agnostic Explanations (LIME) is a popular method that uses a local surrogate model to explain a single instance of machine learning. Its main disadvantages are the instability of the explanations and low local fidelity. In this paper, we propose an original modification to LIME by employing a new perturbed sample generation method for aerodynamic tabular data in regression model, which makes the differences between perturbed samples and the input instance vary in a larger range. We make several comparisons with three subtasks and show that our proposed method results in better metrics.