Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams
van Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2015). Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. In C. Aggarwal, Z.-H. Zhou, A. Tuzhilin, H. Xiong, & X. Wu (Eds.), Proceedings of the 15th IEEE International Conference on Data Mining (pp. 1003–1008). Washington, DC: IEEE. https://doi.org/10.1109/ICDM.2015.55
Permanent Research Commons link: https://hdl.handle.net/10289/10902
Ensembles of classifiers are among the best performing classifiers available in many data mining applications. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of base-level classifier, most often Hoeffding Trees. In this paper, we study the use of heterogeneous ensembles, comprised of fundamentally different model types. Heterogeneous ensembles have proven successful in the classical batch data setting, however they do not easily transfer to the data stream setting. We therefore introduce the Online Performance Estimation framework, which can be used in data stream ensembles to weight the votes of (heterogeneous) ensemble members differently across the stream. Experiments over a wide range of data streams show performance that is competitive with state of the art ensemble techniques, including Online Bagging and Leveraging Bagging. All experimental results from this work are easily reproducible and publicly available on OpenML for further analysis.
This is an author’s accepted version of an article published in the Proceedings of the 15th IEEE International Conference on Data Mining. ©2015 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.