An often used approach for detecting and adapting to con- cept drift when doing classification is to treat the data as i.i.d. and use changes in classification accuracy as an indication of concept drift. In this paper, we take a different perspective and propose a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables. To ensure efficient inference and learning, we resort to a variational Bayes inference scheme. As a proof of concept, we demon- strate and analyze the proposed framework using synthetic data sets as well as a real financial data set from a Spanish bank.
['Hanen Borchani', 'Ana Maria Martinez', 'Andrés R. Masegosa', 'Helge Langseth', 'Thomas Dyhre Nielsen', 'Antonio Salmerón', 'Antonio Fernández', 'Anders L. Madsen', 'Ramón Sáez']
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach