• Corpus ID: 21100604

Towards a more efficient representation of imputation operators in TPOT

@article{Garciarena2018TowardsAM,
  title={Towards a more efficient representation of imputation operators in TPOT},
  author={Unai Garciarena and Alexander Mendiburu and Roberto Santana},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.04407}
}
Automated Machine Learning encompasses a set of meta-algorithms intended to design and apply machine learning techniques (e.g., model selection, hyperparameter tuning, model assessment, etc.). TPOT, a software for optimizing machine learning pipelines based on genetic programming (GP), is a novel example of this kind of applications. Recently we have proposed a way to introduce imputation methods as part of TPOT. While our approach was able to deal with problems with missing data, it can… 

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