Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees

@inproceedings{Tami2018UncertainTD,
  title={Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees},
  author={Myriam Tami and Marianne Clausel and Emilie Devijver and Adrien Dulac and {\'E}ric Gaussier and Stefan Janaqi and M{\'e}riam Ch{\`e}bre},
  year={2018}
}
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty in the output variable, using for example a quantile loss in Random Forests [16]. To the best of our knowledge, no extension has been provided yet for dealing with uncertainties in the input variables, even though such uncertainties are common in practical… CONTINUE READING

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