An Empirical Comparison of Genetic and Decision-Tree Classifiers

Quinlan, J. Ross

doi:10.1016/B978-0-934613-64-4.50019-0

An Empirical Comparison of Genetic and Decision-Tree Classifiers

J. Ross Quinlan

ICML 1988 pp. 135-141

doi:10.1016/B978-0-934613-64-4.50019-0 /icml/1988/quinlan1988icml-empirical/

Abstract

Wilson has reported results obtained by a genetic learning system Boole on a small, abstract learning task. This task is shown to have a property that complicates its analysis by top-down decision tree methods. Nevertheless, experiments with decision tree methods have shown that accurate classifiers can be obtained from comparatively small sets of training examples. Finally, conversion of the decision trees to sets of production rules has led to a significant improvement in classification accuracy for this task.

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Cite

Text

Quinlan. "An Empirical Comparison of Genetic and Decision-Tree Classifiers." International Conference on Machine Learning, 1988. doi:10.1016/B978-0-934613-64-4.50019-0

Markdown

[Quinlan. "An Empirical Comparison of Genetic and Decision-Tree Classifiers." International Conference on Machine Learning, 1988.](https://mlanthology.org/icml/1988/quinlan1988icml-empirical/) doi:10.1016/B978-0-934613-64-4.50019-0

BibTeX

@inproceedings{quinlan1988icml-empirical,
  title     = {{An Empirical Comparison of Genetic and Decision-Tree Classifiers}},
  author    = {Quinlan, J. Ross},
  booktitle = {International Conference on Machine Learning},
  year      = {1988},
  pages     = {135-141},
  doi       = {10.1016/B978-0-934613-64-4.50019-0},
  url       = {https://mlanthology.org/icml/1988/quinlan1988icml-empirical/}
}