Drift Estimation with Graphical Models

Abstract

This paper deals with the issue of concept drift in supervised machine learning. We make use of graphical models to elicit the visible structure of the data and 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 data set. 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.