Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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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 -