On Pruning and Tuning Rules for Associative Classifiers
- Osmar R. Zaiane, University of Alberta (Database)
- Maria-Luiza Antonie, Dept. of Computing Science UofA
The integration of supervised classification and association rules for building classification models is not new. One major advantage is that models are human readable and can be edited. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable model. Pruning unnecessary rules without jeopardizing the classification accuracy is paramount but very challenging. In this paper we study strategies for classification rule pruning in the case of associative classifiers.
Citation
O. Zaiane, M. Antonie. "On Pruning and Tuning Rules for Associative Classifiers". International Conference on Knowledge-Based Intelligence Information , Melbourne, Australia, pp 966-973, September 2005.| Keywords: | |
| Category: | In Conference |
| Web Links: | Springer |
BibTeX
@incollection{Zaiane+Antonie:KES05,
author = {Osmar R. Zaiane and Maria-Luiza Antonie},
title = {On Pruning and Tuning Rules for Associative Classifiers},
Pages = {966-973},
booktitle = {International Conference on Knowledge-Based Intelligence
Information },
year = 2005,
}Last Updated: January 31, 2020Submitted by Sabina P
