Constructive Induction of M-of-N Terms
Abstract
We discuss an approach to constructing composite features during the induction of decision trees. The composite features correspond to m-of-n concepts. There are three goals of this research. First, we explore a family of greedy methods for building m-of-n concepts (one of which, GS, is described in this paper). Second, we show how these concepts can be formed as internal nodes of decision trees, serving as a bias to the learner. Finally, we evaluate the method on several artificially generated and naturally occurring data sets to determine the effects of this bias.
Cite
Text
Murphy and Pazzani. "Constructive Induction of M-of-N Terms." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50040-4Markdown
[Murphy and Pazzani. "Constructive Induction of M-of-N Terms." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/murphy1991icml-constructive/) doi:10.1016/B978-1-55860-200-7.50040-4BibTeX
@inproceedings{murphy1991icml-constructive,
title = {{Constructive Induction of M-of-N Terms}},
author = {Murphy, Patrick M. and Pazzani, Michael J.},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {183-187},
doi = {10.1016/B978-1-55860-200-7.50040-4},
url = {https://mlanthology.org/icml/1991/murphy1991icml-constructive/}
}