Evolving imputation strategies for missing data in classification problems with TPOT

@article{Garciarena2017EvolvingIS,
  title={Evolving imputation strategies for missing data in classification problems with TPOT},
  author={Unai Garciarena and Roberto Santana and Alexander Mendiburu},
  journal={CoRR},
  year={2017},
  volume={abs/1706.01120}
}
Missing data has a ubiquitous presence in real-life applications of machine learning techniques. Imputation methods are algorithms conceived for restoring missing values in the data, based on other entries in the database. The choice of the imputation method has an influence on the performance of the machine learning technique, e.g., it influences the accuracy of the classification algorithm applied to the data. Therefore, selecting and applying the right imputation method is important and… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 33 references

Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016

R. S. Olson, R. J. Urbanowicz, +3 authors J. H. Moore
Proceedings, Part I. pages 123–137. Springer International Publishing, • 2016
View 4 Excerpts
Highly Influenced

Annual ” humies ” awards for humancompetitive results

J. R. Koza.
2017

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