Learning Decision Rules by Randomized Iterative Local Search
Abstract
Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning systems for this problem employ some variation of a greedy separate-and-conquer algorithm, which makes the rules order-dependent, and hence difficult to understand. In this paper, we describe a system called LERILS that learns highly accurate and comprehensible rules from examples using a randomized iterative local search. We compare its performance to C4.5, RIPPER, CN2, G-NET, Smog, and BruteDL, and show that it compares favorably in accuracy and simplicity of hypotheses in a number of domains.
Cite
Text
Chisholm and Tadepalli. "Learning Decision Rules by Randomized Iterative Local Search." International Conference on Machine Learning, 2002.Markdown
[Chisholm and Tadepalli. "Learning Decision Rules by Randomized Iterative Local Search." International Conference on Machine Learning, 2002.](https://mlanthology.org/icml/2002/chisholm2002icml-learning/)BibTeX
@inproceedings{chisholm2002icml-learning,
title = {{Learning Decision Rules by Randomized Iterative Local Search}},
author = {Chisholm, Michael and Tadepalli, Prasad},
booktitle = {International Conference on Machine Learning},
year = {2002},
pages = {75-82},
url = {https://mlanthology.org/icml/2002/chisholm2002icml-learning/}
}