Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning
Abstract
Conventional rule learning algorithms use a single heuristic for evaluating both, rule refinements and rule selection. In this paper, we argue that these two phases should be separated. Moreover, whereas rule selection proceeds in a bottom-up specific-to-general direction, rule refinement typically operates top-down. Hence, in this paper we propose that criteria for evaluating rule refinements should reflect this by operating in an inverted coverage space. We motivate this choice by examples, and show that a suitably adapted rule learning algorithm outperforms its original counter-part on a large set of benchmark problems.
Cite
Text
Stecher et al. "Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_8Markdown
[Stecher et al. "Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/stecher2014ecmlpkdd-separating/) doi:10.1007/978-3-662-44845-8_8BibTeX
@inproceedings{stecher2014ecmlpkdd-separating,
title = {{Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning}},
author = {Stecher, Julius and Janssen, Frederik and Fürnkranz, Johannes},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {114-129},
doi = {10.1007/978-3-662-44845-8_8},
url = {https://mlanthology.org/ecmlpkdd/2014/stecher2014ecmlpkdd-separating/}
}