An Aggregate Learning Approach for Interpretable Semi-Supervised Population Prediction and Disaggregation Using Ancillary Data

Guillaume Derval, Frédéric Docquier, Pierre Schaus

ECML-PKDD 2019 pp. 672-687

doi:10.1007/978-3-030-46133-1_40 /ecmlpkdd/2019/derval2019ecmlpkdd-aggregate/

Abstract

Census data provide detailed information about population characteristics at a coarse resolution. Nevertheless, fine-grained, high-resolution mappings of population counts are increasingly needed to characterize population dynamics and to assess the consequences of climate shocks, natural disasters, investments in infrastructure, development policies, etc. Dissagregating these census is a complex machine learning, and multiple solutions have been proposed in past research. We propose in this paper to view the problem in the context of the aggregate learning paradigm, where the output value for all training points is not known, but where it is only known for aggregates of the points (i.e. in this context, for regions of pixels where a census is available). We demonstrate with a very simple and interpretable model that this method is on par, and even outperforms on some metrics, the state-of-the-art, despite its simplicity.

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Cite

Text

Derval et al. "An Aggregate Learning Approach for Interpretable Semi-Supervised Population Prediction and Disaggregation Using Ancillary Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_40

Markdown

[Derval et al. "An Aggregate Learning Approach for Interpretable Semi-Supervised Population Prediction and Disaggregation Using Ancillary Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/derval2019ecmlpkdd-aggregate/) doi:10.1007/978-3-030-46133-1_40

BibTeX

@inproceedings{derval2019ecmlpkdd-aggregate,
  title     = {{An Aggregate Learning Approach for Interpretable Semi-Supervised Population Prediction and Disaggregation Using Ancillary Data}},
  author    = {Derval, Guillaume and Docquier, Frédéric and Schaus, Pierre},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2019},
  pages     = {672-687},
  doi       = {10.1007/978-3-030-46133-1_40},
  url       = {https://mlanthology.org/ecmlpkdd/2019/derval2019ecmlpkdd-aggregate/}
}