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A Context-Driven Data Weighting Approach for Handling Concept Drift in Classification

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

Standard

A Context-Driven Data Weighting Approach for Handling Concept Drift in Classification. / Barakat, Lida.

Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Wroclaw : Springer, 2015. p. 383-393.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Barakat, L 2015, A Context-Driven Data Weighting Approach for Handling Concept Drift in Classification. in Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Springer, Wroclaw, pp. 383-393. https://doi.org/10.1007/978-3-319-26227-7_36

APA

Barakat, L. (2015). A Context-Driven Data Weighting Approach for Handling Concept Drift in Classification. In Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015 (pp. 383-393). Springer. https://doi.org/10.1007/978-3-319-26227-7_36

Vancouver

Barakat L. A Context-Driven Data Weighting Approach for Handling Concept Drift in Classification. In Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Wroclaw: Springer. 2015. p. 383-393 doi: 10.1007/978-3-319-26227-7_36

Author

Barakat, Lida. / A Context-Driven Data Weighting Approach for Handling Concept Drift in Classification. Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Wroclaw : Springer, 2015. pp. 383-393

Bibtex

@inproceedings{85defb2f802646deb09fa6539ec12ab6,
title = "A Context-Driven Data Weighting Approach for Handling Concept Drift in Classification",
abstract = "Adapting classification models to concept drift is one of the main challenges associated with applying these models in dynamic environments. In particular, the learned concept is not static and may change over time under the influence of varying conditions (i.e. varying context). Unlike existing approaches where only the most recent data are considered for adapting the model, we propose incorporating context awareness into the adaptation process. The goal is to utilise knowledge of relevant context variables to facilitate the selection of more relevant training data. Specifically, we propose to weight each training example based on the degree of similarity with the current context. To detect such similarity, we utilise two approaches: a simple difference between the context variable values and a distribution-based distance metric. The experimental analyses show that such explicit context utilisation results in a more effective data selection strategy and enables to produce more accurate predictions.",
author = "Lida Barakat",
year = "2015",
month = may,
day = "25",
doi = "10.1007/978-3-319-26227-7_36",
language = "English",
isbn = "9783319262253 ",
pages = "383--393",
booktitle = "Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015",
publisher = "Springer",

}

RIS

TY - GEN

T1 - A Context-Driven Data Weighting Approach for Handling Concept Drift in Classification

AU - Barakat, Lida

PY - 2015/5/25

Y1 - 2015/5/25

N2 - Adapting classification models to concept drift is one of the main challenges associated with applying these models in dynamic environments. In particular, the learned concept is not static and may change over time under the influence of varying conditions (i.e. varying context). Unlike existing approaches where only the most recent data are considered for adapting the model, we propose incorporating context awareness into the adaptation process. The goal is to utilise knowledge of relevant context variables to facilitate the selection of more relevant training data. Specifically, we propose to weight each training example based on the degree of similarity with the current context. To detect such similarity, we utilise two approaches: a simple difference between the context variable values and a distribution-based distance metric. The experimental analyses show that such explicit context utilisation results in a more effective data selection strategy and enables to produce more accurate predictions.

AB - Adapting classification models to concept drift is one of the main challenges associated with applying these models in dynamic environments. In particular, the learned concept is not static and may change over time under the influence of varying conditions (i.e. varying context). Unlike existing approaches where only the most recent data are considered for adapting the model, we propose incorporating context awareness into the adaptation process. The goal is to utilise knowledge of relevant context variables to facilitate the selection of more relevant training data. Specifically, we propose to weight each training example based on the degree of similarity with the current context. To detect such similarity, we utilise two approaches: a simple difference between the context variable values and a distribution-based distance metric. The experimental analyses show that such explicit context utilisation results in a more effective data selection strategy and enables to produce more accurate predictions.

U2 - 10.1007/978-3-319-26227-7_36

DO - 10.1007/978-3-319-26227-7_36

M3 - Conference contribution/Paper

SN - 9783319262253

SP - 383

EP - 393

BT - Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

PB - Springer

CY - Wroclaw

ER -