Modeling concept drift: A probabilistic graphical model based approach

Hanen Borchani; Ana Maria Martinez; Andrés R. Masegosa; Helge Langseth; Thomas Dyhre Nielsen; Antonio Salmerón; Antonio Fernández; Anders Læsø Madsen; Ramón Sáez

doi:10.1007/978-3-319-24465-5_7

Modeling concept drift: A probabilistic graphical model based approach

Hanen Borchani, Ana Maria Martinez, Andrés R. Masegosa, Helge Langseth, Thomas Dyhre Nielsen, Antonio Salmerón, Antonio Fernández, Anders Læsø Madsen, Ramón Sáez

Department of Computer Science

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

17 Citations (Scopus)

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Abstract

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 re- sort 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.

Original language	English
Title of host publication	Advances in Intelligent Data Analysis XIV : 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings
Editors	Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen
Publisher	Springer
Publication date	2015
Pages	72-83
ISBN (Print)	978-3-319-24464-8
ISBN (Electronic)	978-3-319-24465-5
DOIs	https://doi.org/10.1007/978-3-319-24465-5_7
Publication status	Published - 2015
Event	International Symposium, IDA 2015 - Saint Etienne, France Duration: 22 Oct 2015 → 24 Oct 2015 Conference number: 14th

Conference

Conference	International Symposium, IDA 2015
Number	14th
Country/Territory	France
City	Saint Etienne
Period	22/10/2015 → 24/10/2015

Series	Lecture Notes in Computer Science
Number	9385
ISSN	0302-9743

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10.1007/978-3-319-24465-5_7

ida_2015Submitted manuscript, 368 KB

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AMIDST: Analysis of MassIve Data STreams - AMIDST
Madsen, A. L., Rommerdahl Bock, A., Nielsen, T. D. & Martinez, A. M.
European Union’s Seventh Framework Programme for research, technological development and demonstration
01/01/2014 → 31/12/2016
Project: Research

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Borchani, H., Martinez, A. M., Masegosa, A. R., Langseth, H., Nielsen, T. D., Salmerón, A., Fernández, A., Madsen, A. L., & Sáez, R. (2015). Modeling concept drift: A probabilistic graphical model based approach. In E. Fromont, T. De Bie, & M. van Leeuwen (Eds.), Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings (pp. 72-83). Springer. https://doi.org/10.1007/978-3-319-24465-5_7

Borchani, Hanen ; Martinez, Ana Maria ; Masegosa, Andrés R. et al. / Modeling concept drift : A probabilistic graphical model based approach. Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. editor / Elisa Fromont ; Tijl De Bie ; Matthijs van Leeuwen. Springer, 2015. pp. 72-83 (Lecture Notes in Computer Science; No. 9385).

@inproceedings{e79a299c4f5a4df98b246a035472af30,

title = "Modeling concept drift: A probabilistic graphical model based approach",

abstract = "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 re- sort 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.",

author = "Hanen Borchani and Martinez, {Ana Maria} and Masegosa, {Andr{\'e}s R.} and Helge Langseth and Nielsen, {Thomas Dyhre} and Antonio Salmer{\'o}n and Antonio Fern{\'a}ndez and Madsen, {Anders L{\ae}s{\o}} and Ram{\'o}n S{\'a}ez",

year = "2015",

doi = "10.1007/978-3-319-24465-5_7",

language = "English",

isbn = "978-3-319-24464-8",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

number = "9385",

pages = "72--83",

editor = "Elisa Fromont and {De Bie}, Tijl and {van Leeuwen}, Matthijs",

booktitle = "Advances in Intelligent Data Analysis XIV",

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note = "International Symposium, IDA 2015 ; Conference date: 22-10-2015 Through 24-10-2015",

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Borchani, H, Martinez, AM, Masegosa, AR, Langseth, H, Nielsen, TD, Salmerón, A, Fernández, A, Madsen, AL & Sáez, R 2015, Modeling concept drift: A probabilistic graphical model based approach. in E Fromont, T De Bie & M van Leeuwen (eds), Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. Springer, Lecture Notes in Computer Science, no. 9385, pp. 72-83, International Symposium, IDA 2015, Saint Etienne, France, 22/10/2015. https://doi.org/10.1007/978-3-319-24465-5_7

Modeling concept drift: A probabilistic graphical model based approach. / Borchani, Hanen; Martinez, Ana Maria; Masegosa, Andrés R. et al.
Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. ed. / Elisa Fromont; Tijl De Bie; Matthijs van Leeuwen. Springer, 2015. p. 72-83 (Lecture Notes in Computer Science; No. 9385).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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AU - Martinez, Ana Maria

AU - Masegosa, Andrés R.

AU - Langseth, Helge

AU - Nielsen, Thomas Dyhre

AU - Salmerón, Antonio

AU - Fernández, Antonio

AU - Madsen, Anders Læsø

AU - Sáez, Ramón

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AB - 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 re- sort 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.

U2 - 10.1007/978-3-319-24465-5_7

DO - 10.1007/978-3-319-24465-5_7

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BT - Advances in Intelligent Data Analysis XIV

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ER -

Borchani H, Martinez AM, Masegosa AR, Langseth H, Nielsen TD, Salmerón A et al. Modeling concept drift: A probabilistic graphical model based approach. In Fromont E, De Bie T, van Leeuwen M, editors, Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. Springer. 2015. p. 72-83. (Lecture Notes in Computer Science; No. 9385). doi: 10.1007/978-3-319-24465-5_7

Modeling concept drift: A probabilistic graphical model based approach

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Projects

AMIDST: Analysis of MassIve Data STreams - AMIDST

Cite this