Abstract
This paper reports the use of the PAELLA algorithm in the context of weighted regression. First, an experiment comparing this new approach versus probabilistic macro sampling is reported, as a natural extension of previous work. Then another different experiment is reported where this approach is tested against a state of the art regression technique. Both experiments provide satisfactory results.
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Notes
- 1.
Code available in http://www.optimal-group.org/Resource/WLETSVR.html.
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Acknowledgements
We gratefully acknowledge the financial support of Spanish Ministerio de Economía, Industria y Competitividad through grant DPI2016-79960-C3-2-P. We would like to also express our gratitude to Castilla y León Supercomputing Center whose cooperation allowed us to run around one million neural network trainings for the experiments reported on this paper.
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Castejón-Limas, M. et al. (2018). PAELLA as a Booster in Weighted Regression. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_25
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