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Similarity encoding for learning with dirty categorical variables

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  1. Special Issue of the ECML PKDD 2018 Journal Track

Abstract

For statistical learning, categorical variables in a table are usually considered as discrete entities and encoded separately to feature vectors, e.g., with one-hot encoding. “Dirty” non-curated data give rise to categorical variables with a very high cardinality but redundancy: several categories reflect the same entity. In databases, this issue is typically solved with a deduplication step. We show that a simple approach that exposes the redundancy to the learning algorithm brings significant gains. We study a generalization of one-hot encoding, similarity encoding, that builds feature vectors from similarities across categories. We perform a thorough empirical validation on non-curated tables, a problem seldom studied in machine learning. Results on seven real-world datasets show that similarity encoding brings significant gains in predictive performance in comparison with known encoding methods for categories or strings, notably one-hot encoding and bag of character n-grams. We draw practical recommendations for encoding dirty categories: 3-gram similarity appears to be a good choice to capture morphological resemblance. For very high-cardinalities, dimensionality reduction significantly reduces the computational cost with little loss in performance: random projections or choosing a subset of prototype categories still outperform classic encoding approaches.

Keywords

Dirty data Categorical variables Statistical learning String similarity measures 

Notes

Acknowledgements

We would like to acknowledge the excellent feedback from the reviewers. This work was funded by the Wendelin and DirtyData (ANR-17-CE23-0018) grants.

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Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Inria, Parietal teamPalaiseauFrance
  2. 2.Linear Accelerator Laboratory, CNRSOrsayFrance

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