Corpus ID: 214774764

Surrogate-assisted performance tuning of knowledge discovery algorithms: application to clinical pathway evolutionary modeling

@article{Funkner2020SurrogateassistedPT,
  title={Surrogate-assisted performance tuning of knowledge discovery algorithms: application to clinical pathway evolutionary modeling},
  author={Anastasia A. Funkner and Aleksey N. Yakovlev and Sergey V. Kovalchuk},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.01123}
}
  • Anastasia A. Funkner, Aleksey N. Yakovlev, Sergey V. Kovalchuk
  • Published 2020
  • Mathematics, Computer Science
  • ArXiv
  • The paper proposes an approach for surrogate-assisted tuning of knowledge discovery algorithms. The approach is based on the prediction of both the quality and performance of the target algorithm. The prediction is furtherly used as objectives for the optimization and tuning of the algorithm. The approach is investigated using clinical pathways (CP) discovery problem resolved using the evolutionary-based clustering of electronic health records (EHR). Target algorithm and the proposed approach… CONTINUE READING

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