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Agnostic Explanation of Model Change based on Feature Importance

dc.contributor.authorMuschalik, Maximilian
dc.contributor.authorFumagalli, Fabian
dc.contributor.authorHammer, Barbara
dc.contributor.authorHüllermeier, Eyke
dc.date.accessioned2023-01-18T13:08:28Z
dc.date.available2023-01-18T13:08:28Z
dc.date.issued2022
dc.description.abstractExplainable Artificial Intelligence (XAI) has mainly focused on static learning tasks so far. In this paper, we consider XAI in the context of online learning in dynamic environments, such as learning from real-time data streams, where models are learned incrementally and continuously adapted over the course of time. More specifically, we motivate the problem of explaining model change , i.e. explaining the difference between models before and after adaptation, instead of the models themselves. In this regard, we provide the first efficient model-agnostic approach to dynamically detecting, quantifying, and explaining significant model changes. Our approach is based on an adaptation of the well-known Permutation Feature Importance (PFI) measure. It includes two hyperparameters that control the sensitivity and directly influence explanation frequency, so that a human user can adjust the method to individual requirements and application needs. We assess and validate our method’s efficacy on illustrative synthetic data streams with three popular model classes.de
dc.identifier.doi10.1007/s13218-022-00766-6
dc.identifier.pissn1610-1987
dc.identifier.urihttp://dx.doi.org/10.1007/s13218-022-00766-6
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40067
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 36, No. 0
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectConcept Drift
dc.subjectData Streams
dc.subjectExplainable Artificial Intelligence
dc.subjectExplaining Model Change
dc.subjectIncremental Learning
dc.titleAgnostic Explanation of Model Change based on Feature Importancede
dc.typeText/Journal Article
gi.citation.endPage224
gi.citation.startPage211

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