1. Imbalanced regression using regressor-classifier ensembles
- Author
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Orhobor, OI, Grinberg, NF, Soldatova, LN, King, RD, Orhobor, OI [0000-0003-1178-611X], and Apollo - University of Cambridge Repository
- Subjects
Artificial Intelligence ,Machine learning ,Imbalanced data ,Ensemble regression ,Software - Abstract
Acknowledgements: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) UK through the ACTION on cancer Grant (EP/R022925/1, EP/R022941/1). The computational resources were provided by the Swedish National Infrastructure for Computing (SNIC) at the Chalmers University of Technology partially funded by the Swedish Research Council through Grant Agreement No. 2018-05973. Prof. King acknowledges the support of the Knut and Alice Wallenberg Foundation Wallenberg Autonomous Systems and Software Program (WASP). N. F. Grinberg would like to acknowledge funding from the Wellcome Trust (WT107881) and the MRC (MC_UU_00002/4)., We present an extension to the federated ensemble regression using classification algorithm, an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We evaluated the extension using four classifiers and four regressors, two discretizers, and 119 responses from a wide variety of datasets in different domains. Additionally, we compared our algorithm to two resampling methods aimed at addressing imbalanced datasets. Our results show that the proposed extension is highly unlikely to perform worse than the base case, and on average outperforms the two resampling methods with significant differences in performance.
- Published
- 2022