iSAGE: An Incremental Version of SAGE for Online Explanation on Data
Streams
- URL: http://arxiv.org/abs/2303.01181v2
- Date: Wed, 14 Jun 2023 18:10:04 GMT
- Title: iSAGE: An Incremental Version of SAGE for Online Explanation on Data
Streams
- Authors: Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke
H\"ullermeier
- Abstract summary: iSAGE is a time- and memory-efficient incrementalization of SAGE.
We show that iSAGE adheres to similar theoretical properties as SAGE.
- Score: 8.49072000414555
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Existing methods for explainable artificial intelligence (XAI), including
popular feature importance measures such as SAGE, are mostly restricted to the
batch learning scenario. However, machine learning is often applied in dynamic
environments, where data arrives continuously and learning must be done in an
online manner. Therefore, we propose iSAGE, a time- and memory-efficient
incrementalization of SAGE, which is able to react to changes in the model as
well as to drift in the data-generating process. We further provide efficient
feature removal methods that break (interventional) and retain (observational)
feature dependencies. Moreover, we formally analyze our explanation method to
show that iSAGE adheres to similar theoretical properties as SAGE. Finally, we
evaluate our approach in a thorough experimental analysis based on
well-established data sets and data streams with concept drift.
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