Drift Estimation with Graphical Models
This paper tackles the problem of concept drift detection for machine learning practitioners, offering a method that is independent of the specific supervised learning model.
This paper addresses concept drift in supervised machine learning by using graphical models to identify changes in data structure. It proposes a method to detect drift by observing the creation and disappearance of links in graphical models over time, independent of the specific supervised learning model. The method is evaluated using real-world data from the Australian Electric market.
This paper deals with the issue of concept drift in supervised machine learn-ing. We make use of graphical models to elicit the visible structure of the dataand we infer from there changes in the hidden context. Differently from previous concept-drift detection methods, this application does not depend on the supervised machine learning model in use for a specific target variable, but it tries to assess the concept drift as independent characteristic of the evolution of a dataset. Specifically, we investigate how a graphical model evolves by looking at the creation of new links and the disappearing of existing ones in different time periods. The paper suggests a method that highlights the changes and eventually produce a metric to evaluate the stability over time. The paper evaluate the method with real world data on the Australian Electric market.