Learning Model Rules from High-Speed Data Streams
Abstract
Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule in AMRules uses a Page-Hinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our algorithm with other streaming regression algorithms. © 2013 IJCAI.
Cite
Text
Almeida et al. "Learning Model Rules from High-Speed Data Streams." International Joint Conference on Artificial Intelligence, 2013.Markdown
[Almeida et al. "Learning Model Rules from High-Speed Data Streams." International Joint Conference on Artificial Intelligence, 2013.](https://mlanthology.org/ijcai/2013/almeida2013ijcai-learning/)BibTeX
@inproceedings{almeida2013ijcai-learning,
title = {{Learning Model Rules from High-Speed Data Streams}},
author = {Almeida, Ezilda and Ferreira, Carlos Abreu and Gama, João},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2013},
pages = {10},
url = {https://mlanthology.org/ijcai/2013/almeida2013ijcai-learning/}
}