ML Anthology

Breeding Decision Trees Using Evolutionary Techniques

Athanassios Papagelis, Dimitrios Kalles
ICML 2001 pp. 393-400
/icml/2001/papagelis2001icml-breeding/

Abstract

We explore the use of genetic algorithms to directly evolve classification decision trees. We argue on the suitability of such a concept learner due to its ability to efficiently search complex hypotheses spaces and discover conditionally dependent as well as irrelevant attributes. The performance of the system is measured on a set of artificial and standard discretized concept-learning problems and compared with the performance of two known algorithms (C4.5, OneR). We demonstrate that the derived hypotheses of standard algorithms can substantially deviate from the optimum. This deviation is partly because of their non-universal procedural bias and it can be reduced using global metrics of tree quality like the one proposed. 1

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Text

Papagelis and Kalles. "Breeding Decision Trees Using Evolutionary Techniques." International Conference on Machine Learning, 2001.

Markdown

[Papagelis and Kalles. "Breeding Decision Trees Using Evolutionary Techniques." International Conference on Machine Learning, 2001.](https://mlanthology.org/icml/2001/papagelis2001icml-breeding/)

BibTeX

@inproceedings{papagelis2001icml-breeding,
  title     = {{Breeding Decision Trees Using Evolutionary Techniques}},
  author    = {Papagelis, Athanassios and Kalles, Dimitrios},
  booktitle = {International Conference on Machine Learning},
  year      = {2001},
  pages     = {393-400},
  url       = {https://mlanthology.org/icml/2001/papagelis2001icml-breeding/}
}