Sig-Lime: A Signal-Based Enhancement of Lime Explanation Technique

Abstract

Interpreting machine learning models is facilitated by the widely employed locally interpretable model-agnostic explanation (LIME) technique. However, when extending LIME to signal data, its credibility falters due to perturbation techniques used to generate local datasets. These techniques disrupt temporal dependencies among features, leading to unrealistic data points and potentially misleading explanations. Additionally, LIME faces instability and local fidelity issues, limiting its suitability for real-world applications. The absence of a dedicated LIME package tailored for interpreting signal data further diminishes comprehensibility, especially when applied to models trained on such data. In this paper, we introduce Signalbased LIME (Sig-LIME) to address these limitations. Sig-LIME leverages a novel data generation technique that captures temporal dependence among features, enhancing credibility and stability. It combines a random forest model and heatmaps to provide illuminating explanations for predictions drawn from electrocardiogram (ECG) signals, improving model transparency. Empirical findings underscore the enhanced interpretability and comprehension of model predictions attained by Sig-LIME compared to baseline LIME. Our quantitative evaluation based on an analysis of variance (ANOVA) framework, reveals a notable improvement in stability with Sig-LIME, evidenced by an f-statistic of 0.0 and p-values of 1, indicating a complete absence of variation between multiple runs. Regarding local fidelity, Sig-LIME surpasses the baseline LIME, exhibiting a lower average Euclidean distance of 0.49 compared to 17.24. Sig-LIME excels in generating data more akin to the original, achieving remarkable stability and significantly enhancing credibility and local fidelity in the explanations it generates.