Context-aware regression from distributed sources

Héctor Allende-Cid; Claudio Moraga; Héctor Allende; Raúl Monge

doi:10.1007/978-3-319-01571-2_3

Context-aware regression from distributed sources

Héctor Allende-Cid, Claudio Moraga, Héctor Allende, Raúl Monge

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In this paper we present a distributed regression framework to model data with different contexts. Different context is defined as the change of the underlying laws of probability in the distributed sources. Most state of the art methods do not take into account the different context and assume that the data comes from the same statistical distribution. We propose an aggregation scheme for models that are in the same neighborhood in terms of statistical divergence.We conduct experiments with synthetic data sets to validate our proposal. Our proposed algorithm outperforms other models that follow a traditional approach.

Original language	English
Title of host publication	Intelligent Distributed Computing VII
Subtitle of host publication	Proceedings of the 7th International Smposium on Intelligent Distributed Computing - IDC 2013, Prague, Czech Republi
Publisher	Springer Verlag
Pages	17-22
Number of pages	6
ISBN (Print)	9783319015705
DOIs	https://doi.org/10.1007/978-3-319-01571-2_3
State	Published - 2014
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	511
ISSN (Print)	1860-949X

Keywords

Context-aware Regression
Distributed Regression
Divergence Measures

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10.1007/978-3-319-01571-2_3

Cite this

Allende-Cid, H., Moraga, C., Allende, H., & Monge, R. (2014). Context-aware regression from distributed sources. In Intelligent Distributed Computing VII: Proceedings of the 7th International Smposium on Intelligent Distributed Computing - IDC 2013, Prague, Czech Republi (pp. 17-22). (Studies in Computational Intelligence; Vol. 511). Springer Verlag. https://doi.org/10.1007/978-3-319-01571-2_3

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title = "Context-aware regression from distributed sources",

abstract = "In this paper we present a distributed regression framework to model data with different contexts. Different context is defined as the change of the underlying laws of probability in the distributed sources. Most state of the art methods do not take into account the different context and assume that the data comes from the same statistical distribution. We propose an aggregation scheme for models that are in the same neighborhood in terms of statistical divergence.We conduct experiments with synthetic data sets to validate our proposal. Our proposed algorithm outperforms other models that follow a traditional approach.",

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Allende-Cid, H, Moraga, C, Allende, H & Monge, R 2014, Context-aware regression from distributed sources. in Intelligent Distributed Computing VII: Proceedings of the 7th International Smposium on Intelligent Distributed Computing - IDC 2013, Prague, Czech Republi. Studies in Computational Intelligence, vol. 511, Springer Verlag, pp. 17-22. https://doi.org/10.1007/978-3-319-01571-2_3

Context-aware regression from distributed sources. / Allende-Cid, Héctor; Moraga, Claudio; Allende, Héctor et al.
Intelligent Distributed Computing VII: Proceedings of the 7th International Smposium on Intelligent Distributed Computing - IDC 2013, Prague, Czech Republi. Springer Verlag, 2014. p. 17-22 (Studies in Computational Intelligence; Vol. 511).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Context-aware regression from distributed sources

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AU - Moraga, Claudio

AU - Allende, Héctor

AU - Monge, Raúl

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AB - In this paper we present a distributed regression framework to model data with different contexts. Different context is defined as the change of the underlying laws of probability in the distributed sources. Most state of the art methods do not take into account the different context and assume that the data comes from the same statistical distribution. We propose an aggregation scheme for models that are in the same neighborhood in terms of statistical divergence.We conduct experiments with synthetic data sets to validate our proposal. Our proposed algorithm outperforms other models that follow a traditional approach.

KW - Context-aware Regression

KW - Distributed Regression

KW - Divergence Measures

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Context-aware regression from distributed sources

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Keywords

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