Improving LIME Stability via Density-Awareness: Evaluation and Comparison of AKDE-LIME
Grigorios Tzionis, Georgia Kougka, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris, Maro Vlachopoulou
TL;DR
This paper introduces AKDE-LIME, a more stable version of LIME for generating reliable local explanations in machine learning models.
Contribution
AKDE-LIME introduces a density-aware weighting scheme to improve the stability of local explanations.
Findings
AKDE-LIME produces significantly more stable explanations than standard LIME across various noise levels.
AKDE-LIME's performance is comparable to or better than TreeSHAP and Anchor in robustness and stability.
The method is effective on diverse tree-based models using real-world data.
Abstract
This paper addresses the critical instability of Local Interpretable Model-agnostic Explanations (LIME). We introduce Adaptive Kernel Density Estimation LIME (AKDE-LIME), a novel approach that enhances local explanation stability by incorporating a density-aware weighting scheme. Unlike LIME’s standard proximity kernel, AKDE-LIME combines distance weighting with a Kernel Density Estimate (KDE) of the local sample distribution, assigning more representative weights to generated perturbations. We conduct a comprehensive evaluation of AKDE-LIME against LIME, TreeSHAP, and Anchor on five diverse tree-based models using a real-world dataset. Assessing performance on Stability and Robustness metrics across a matrix of noise levels (5% to 20%), our results consistently demonstrate that AKDE-LIME produces significantly more stable and robust explanations than standard LIME under all conditions.…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Adversarial Robustness in Machine Learning · Advanced Graph Neural Networks