Modeling Concept Drift: A Probabilistic Graphical Model Based Approach

An often used approach for detecting and adapting to concept 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 gra...

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Bibliographic Details
Published inAdvances in Intelligent Data Analysis XIV Vol. 9385; pp. 72 - 83
Main Authors Borchani, Hanen, Martínez, Ana M., Masegosa, Andrés R., Langseth, Helge, Nielsen, Thomas D., Salmerón, Antonio, Fernández, Antonio, Madsen, Anders L., Sáez, Ramón
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Concept Drift
Image processing
Latent Variable
Message Passing Scheme
Pattern recognition
Probabilistic Graphical Model
Spanish Bank
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Summary:An often used approach for detecting and adapting to concept 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 demonstrate and analyze the proposed framework using synthetic data sets as well as a real financial data set from a Spanish bank.
Bibliography:H. Borchani, A.M. Martínez, and A.R. Masegosa—These authors are considered as first authors and contributed equally to this work.
ISBN:3319244647
9783319244648
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-24465-5_7