Context Identification and Exploitation in Dynamic Data Mining - An Application to Classifying Electricity Price Changes

Abstract

Adapting classification models to concept changes is one of the main challenges associated with learning in dynamic environments, where the definition of the target concept may change over time under the influence of varying contextual factors. Existing adaptive approaches, however, are limited in terms of the extent to which such contextual factors are explicitly identified and utilised, despite their importance. In response, we propose an informationtheoretic- based approach for systematic context identification, aiming to learn from data the contextual characteristics of the domain by identifying the context variables contributing to concept changes. Such explicit identification of context enables capturing the causes of drift, and hence facilitating more effective adaptation. We conduct experimental analyses to demonstrate the effectiveness of the approach on both simulated datasets with various change scenarios, and on an actual benchmark dataset from an electricity market.