ML Anthology

Concept Simplification and Prediction Accuracy

Douglas H. Fisher, Jeffrey C. Schlimmer
ICML 1988 pp. 22-28
doi:10.1016/B978-0-934613-64-4.50007-4 /icml/1988/fisher1988icml-concept/

Abstract

A recently reported phenomenon in machine concept learning is that concept descriptions can be simplified with little ill-effect (or even positive effects) on classification accuracy, but there has been little qualification of this observation. Experiments using Quinlan's learning from examples system, ID3, and Fisher's conceptual clustering system, COBWEB, suggest that the benefits of simplification vary with the amount of training and with the statistical dependence of concept members on defining attributes.

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Cite

Text

Fisher and Schlimmer. "Concept Simplification and Prediction Accuracy." International Conference on Machine Learning, 1988. doi:10.1016/B978-0-934613-64-4.50007-4

Markdown

[Fisher and Schlimmer. "Concept Simplification and Prediction Accuracy." International Conference on Machine Learning, 1988.](https://mlanthology.org/icml/1988/fisher1988icml-concept/) doi:10.1016/B978-0-934613-64-4.50007-4

BibTeX

@inproceedings{fisher1988icml-concept,
  title     = {{Concept Simplification and Prediction Accuracy}},
  author    = {Fisher, Douglas H. and Schlimmer, Jeffrey C.},
  booktitle = {International Conference on Machine Learning},
  year      = {1988},
  pages     = {22-28},
  doi       = {10.1016/B978-0-934613-64-4.50007-4},
  url       = {https://mlanthology.org/icml/1988/fisher1988icml-concept/}
}