Search Results for author:
Found 5 papers, 2 papers with code
Model Based Explanations of Concept Drift
To do so, we propose a methodology to reduce the explanation of concept drift to an explanation of models that are trained in a suitable way extracting relevant information regarding the drift.
Combining self-labeling and demand based active learning for non-stationary data streams
Learning from non-stationary data streams is a research direction that gains increasing interest as more data in form of streams becomes available, for example from social media, smartphones, or industrial process monitoring.
On the Change of Decision Boundaries and Loss in Learning with Concept Drift
More precisely, we relate a change of the ITTE to the presence of real drift, i. e., a changed posterior, and to a change of the training result under the assumption of optimality.
Explaining Reject Options of Learning Vector Quantization Classifiers
While machine learning models are usually assumed to always output a prediction, there also exist extensions in the form of reject options which allow the model to reject inputs where only a prediction with an unacceptably low certainty would be possible.
Evaluating Robustness of Counterfactual Explanations
Counterfactual explanations explain a behavior to the user by proposing actions -- as changes to the input -- that would cause a different (specified) behavior of the system.
