Corpus ID: 203641977

Silas: High Performance, Explainable and Verifiable Machine Learning

@article{Bride2019SilasHP,
  title={Silas: High Performance, Explainable and Verifiable Machine Learning},
  author={Hadrien Bride and Zhe Hou and Jie Dong and Jin Song Dong and Seyed Mohammad Mirjalili},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.01382}
}
  • Hadrien Bride, Zhe Hou, +2 authors Seyed Mohammad Mirjalili
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • This paper introduces a new classification tool named Silas, which is built to provide a more transparent and dependable data analytics service. A focus of Silas is on providing a formal foundation of decision trees in order to support logical analysis and verification of learned prediction models. This paper describes the distinct features of Silas: The Model Audit module formally verifies the prediction model against user specifications, the Enforcement Learning module trains prediction… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    References

    Publications referenced by this paper.