# A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation

arXiv: https://arxiv.org/abs/2002.07434 · HTML: https://mlsanity.com/paper/2002.07434/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models](https://mlsanity.com/paper/2204.03497/) (2022)
- [GLIME: General, Stable and Local LIME Explanation](https://mlsanity.com/paper/2311.15722/) (2023)
- [Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View](https://mlsanity.com/paper/2112.02625/) (2021)
- [Which LIME should I trust? Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2503.24365/) (2025)
- [LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations](https://mlsanity.com/paper/2111.08094/) (2021)
- [The Generalizability of Explanations](https://mlsanity.com/paper/2302.11965/) (2023)
- [EVolutionary Independent DEtermiNistiC Explanation](https://mlsanity.com/paper/2501.16357/) (2025)
- [MindfulLIME: A Stable Solution for Explanations of Machine Learning Models with Enhanced Localization Precision -- A Medical Image Case Study](https://mlsanity.com/paper/2503.20758/) (2025)
- [Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning](https://mlsanity.com/paper/2506.20916/) (2025)
- [MUPAX: Multidimensional Problem Agnostic eXplainable AI](https://mlsanity.com/paper/2507.13090/) (2025)
- [Beyond Linear Surrogates: High-Fidelity Local Explanations for Black-Box Models](https://mlsanity.com/paper/2512.05556/) (2025)
- [Enhancing LIME using Neural Decision Trees](https://mlsanity.com/paper/2603.20919/) (2026)

## Abstract (arXiv)

Explainability is a gateway between Artificial Intelligence and society as the current popular deep learning models are generally weak in explaining the reasoning process and prediction results. Local Interpretable Model-agnostic Explanation (LIME) is a recent technique that explains the predictions of any classifier faithfully by learning an interpretable model locally around the prediction. However, the sampling operation in the standard implementation of LIME is defective. Perturbed samples are generated from a uniform distribution, ignoring the complicated correlation between features. This paper proposes a novel Modified Perturbed Sampling operation for LIME (MPS-LIME), which is formalized as the clique set construction problem. In image classification, MPS-LIME converts the superpixel image into an undirected graph. Various experiments show that the MPS-LIME explanation of the black-box model achieves much better performance in terms of understandability, fidelity, and efficiency.