Decision tree classification with bounded number of errors

@article{Saettler2017DecisionTC,
  title={Decision tree classification with bounded number of errors},
  author={Aline Medeiros Saettler and Eduardo Sany Laber and Felipe de A. Mello Pereira},
  journal={Inf. Process. Lett.},
  year={2017},
  volume={127},
  pages={27-31}
}
Oblivious decision trees are decision trees where every node in the same level is associated with the same attribute. These trees have been studied in the context of feature selection. In this paper, we study the problem of constructing an oblivious decision tree that incurs at most k classification errors, where k is a given integer. We present a randomized rounding algorithm that, given a parameter 0 < < 1/2, builds an oblivious decision tree with cost at most (3/(1 − 2 )) ln(n)OPT (I) and… CONTINUE READING

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