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Adaptive XGBoost for evolving data streams

Abstract
Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is eXtreme Gradient Boosting (XGB). We present an adaptation of XGB for classification of evolving data streams. In this setting, new data arrives over time and the relationship between the class and the features may change in the process, thus exhibiting concept drift. The proposed method creates new members of the ensemble from mini-batches of data as new data becomes available. The maximum ensemble size is fixed, but learning does not stop when this size is reached because the ensemble is updated on new data to ensure consistency with the current concept. We also explore the use of concept drift detection to trigger a mechanism to update the ensemble. We test our method on real and synthetic data with concept drift and compare it against batch-incremental and instance-incremental classification methods for data streams.
Type
Conference Contribution
Type of thesis
Series
Citation
Montiel, J., Mitchell, R., Frank, E., Pfahringer, B., Abdessalem, T., & Bifet, A. (2020). Adaptive XGBoost for evolving data streams. In Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). Washington, DC, USA: IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207555
Date
2020
Publisher
IEEE
Degree
Supervisors
Rights
This is an author’s accepted version of an article published in the Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN). © 2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.