Rule-Based Machine Learning Methods for Functional Prediction
Abstract
We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.
Cite
Text
Weiss and Indurkhya. "Rule-Based Machine Learning Methods for Functional Prediction." Journal of Artificial Intelligence Research, 1995. doi:10.1613/JAIR.199Markdown
[Weiss and Indurkhya. "Rule-Based Machine Learning Methods for Functional Prediction." Journal of Artificial Intelligence Research, 1995.](https://mlanthology.org/jair/1995/weiss1995jair-rulebased/) doi:10.1613/JAIR.199BibTeX
@article{weiss1995jair-rulebased,
title = {{Rule-Based Machine Learning Methods for Functional Prediction}},
author = {Weiss, Sholom M. and Indurkhya, Nitin},
journal = {Journal of Artificial Intelligence Research},
year = {1995},
pages = {383-403},
doi = {10.1613/JAIR.199},
volume = {3},
url = {https://mlanthology.org/jair/1995/weiss1995jair-rulebased/}
}