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2020

An Extension of LIME with Improvement of Interpretability and Fidelity

Shi, Sheng, Du, Yangzhou, Fan, Wei

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While deep learning makes significant achievements in Artificial Intelligence (AI), the lack of transparency has limited its broad application in various vertical domains.

Explainability is not only a gateway between AI and real world, but also a powerful feature to detect flaw of the models and bias of the data.
Local Interpretable Model-agnostic Explanation (LIME) is a widely-accepted technique that explains the prediction of any classifier faithfully by learning an interpretable model locally around the predicted instance.
As an extension of LIME, this paper proposes an high-interpretability and high-fidelity local explanation method, known as Local Explanation using feature Dependency Sampling and Nonlinear Approximation (LEDSNA).

An Extension of LIME with Improvement of Interpretability and Fidelity · Around

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