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

Abstract

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.

References in corpus (1)

• Model-Agnostic Interpretability of Machine Learning

Cited by in corpus (4)

• Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
• BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
• LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations
• SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models