Automatic Discovery of the Statistical Types of Variables in a Dataset.
Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017
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Valera, I., & Ghahramani, Z. (2017). Automatic Discovery of the Statistical Types of Variables in a Dataset.. ICML, 70 3521-3529. http://proceedings.mlr.press/v70/
A common practice in statistics and machine learning is to assume that the statistical data types (e.g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known. However, as the availability of real- world data increases, this assumption becomes too restrictive. Data are often heterogeneous, complex, and improperly or incompletely documented. Surprisingly, despite their practical importance, there is still a lack of tools to automatically discover the statistical types of, as well as appropriate likelihood (noise) models for, the variables in a dataset. In this paper, we fill this gap by proposing a Bayesian method, which accurately discovers the statistical data types in both synthetic and real data.
Humboldt Research Fellowship for Postdoctoral Researchers, which funded this research during her stay at the Max Planck Institute for Software Systems. ATI Grant EP/N510129/1 EPSRC Grant EP/N014162/1 Google
External link: http://proceedings.mlr.press/v70/
This record's URL: https://www.repository.cam.ac.uk/handle/1810/269733