Redundancy Reduction: Does it Help Associative Classifiers?
- Luiza Antonie
- Osmar R. Zaiane, University of Alberta (Database)
- Robert Holte, Department of Computing Science, University of Alberta
The number of classification rules discovered in associative classification is typically quite large. In addition, these rules contain redundant information since classification rules are obtained from mined frequent item sets and the latter are known to be repetitive. In this paper we investigate through an empirical study the performance of associative classifiers when the classification rules are generated from frequent, closed and maximal item sets. We show that maximal item sets substantially reduce the number of classification rules without jeopardizing the accuracy of the classifier. Our extensive analysis demonstrates that the performance remains stable and even improves in some cases. Our analysis using cost curves also provides recommendations on when it is appropriate to remove redundancy in frequent item sets.
Citation
L. Antonie, O. Zaiane, R. Holte. "Redundancy Reduction: Does it Help Associative Classifiers?". ACM Symposium on Applied Computing, Pisa, Italy, pp 867-874, April 2016.| Keywords: | |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Antonie+al:16,
author = {Luiza Antonie and Osmar R. Zaiane and Robert Holte},
title = {Redundancy Reduction: Does it Help Associative Classifiers?},
Pages = {867-874},
booktitle = {ACM Symposium on Applied Computing},
year = 2016,
}Last Updated: November 13, 2019Submitted by Sabina P
