Impact of learning set quality and size on decision tree performances

@article{Sebban2000ImpactOL,
  title={Impact of learning set quality and size on decision tree performances},
  author={Marc Sebban and Richard Nock and Jean-Hugues Chauchat and Ricco Rakotomalala},
  journal={Int. J. Comput. Syst. Signal},
  year={2000},
  volume={1},
  pages={85-105}
}
The quality of a decision tree is usually evaluated through its complexity and its generalization accuracy. Tree-simpliÞcation procedures aim at optimizing these two performance criteria. Among them, data reduction techniques differ from pruning by their simpliÞcation strategy. Actually, while pruning algorithms directly control tree size to combat the overÞtting problem, data reduction techniques perform a data preprocessing prior to decision tree construction to improve the learning set… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 20 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 28 references

Nock . Instance pruning as an information preserving problem

  • M. Sebban, R.
  • Seventeenth International Conference on Machine…
  • 2000

On feature selection: a new Þlter model

  • M. Sebban
  • In Twelfth International Florida AI Research…
  • 1999
1 Excerpt

Similar Papers

Loading similar papers…