Jan Brabec
Cenek Skarda


Presented herein is a novel algorithm for inference on decision forest models that increases the robustness of the decisions in the presence of missing features in the data. The proposed algorithm ensures that tree decisions are supported by a minimal amount of non-missing features. Experiments have demonstrated that the proposed algorithm not only increases the robustness of the model, but also increase the model’s predictive performance.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.