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2019

A study of data and label shift in the LIME framework

Rahnama, Amir Hossein Akhavan, Boström, Henrik

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Understand

LIME is a popular approach for explaining a black-box prediction through an interpretable model that is trained on instances in the vicinity of the predicted instance.

To generate these instances, LIME randomly selects a subset of the non-zero features of the predicted instance.
After that, the perturbed instances are fed into the black-box model to obtain labels for these, which are then used for training the interpretable model.
In this study, we present a systematic evaluation of the interpretable models that are output by LIME on the two use-cases that were considered in the original paper introducing the approach; text classification and object detection.

A study of data and label shift in the LIME framework · Around

Built on

Ann-dt: an algorithm for extraction of decision trees from artificial neural networks
Gregor PJ Schmitz, Chris Aldrich, and Francois S Gouws · 1999
Earlier work this paper cites.
Dataset shift in machine learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.

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Then

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