Bottom-up Induction of Oblivious Read-Once Decision Graphs
Abstract
We investigate the use of oblivious, read- once decision graphs as structures for representing concepts over discrete domains, and present a bottom-up, hill-climbing algorithm for inferring these structures from labelled instances. The algorithm is robust with respect to irrelevant attributes, and experimental results show that it performs well on problems considered difficult for symbolic induction methods, such as the Monk's problems and parity.
Cite
Text
Kohavi. "Bottom-up Induction of Oblivious Read-Once Decision Graphs." European Conference on Machine Learning, 1994. doi:10.1007/3-540-57868-4_56Markdown
[Kohavi. "Bottom-up Induction of Oblivious Read-Once Decision Graphs." European Conference on Machine Learning, 1994.](https://mlanthology.org/ecmlpkdd/1994/kohavi1994ecml-bottomup/) doi:10.1007/3-540-57868-4_56BibTeX
@inproceedings{kohavi1994ecml-bottomup,
title = {{Bottom-up Induction of Oblivious Read-Once Decision Graphs}},
author = {Kohavi, Ron},
booktitle = {European Conference on Machine Learning},
year = {1994},
pages = {154-169},
doi = {10.1007/3-540-57868-4_56},
url = {https://mlanthology.org/ecmlpkdd/1994/kohavi1994ecml-bottomup/}
}